public static void RunMain()
{
StartScene scene = new StartScene();
StartLayer layer = new StartLayer();
scene.addChild(layer);
CCDirector.sharedDirector().runWithScene(scene);
}
private void Awake()
{
StartLayer.SetActive(true);
if (_instance == null)
{
_instance = this;
DontDestroyOnLoad(gameObject);
}
else
{
DestroyImmediate(gameObject);
}
}
static void Main(string[] args)
{
//Read CL arguments
for (int i = 0; i < args.Length; i++)
{
if (args[i] == "-d")
{
deviceID = int.Parse(args[++i]);
}
if (args[i] == "-lr")
{
learning_rate = double.Parse(args[++i], System.Globalization.NumberStyles.AllowDecimalPoint, CultureInfo.InvariantCulture);
}
if (args[i] == "-iso")
{
ISO = args[++i];
}
if (args[i] == "-t")
{
crosscheck = true;
}
if (args[i] == "-w")
{
warmStart = int.Parse(args[++i]);
Console.WriteLine("Start with epoch " + warmStart);
}
if (args[i] == "-s")
{
saveImages = true;
}
}
Console.WriteLine("Using device ID: " + deviceID);
Console.WriteLine("Learning rate: " + learning_rate);
//Init Cuda stuff
ctx = new PrimaryContext(deviceID);
ctx.SetCurrent();
Console.WriteLine("Context created");
CUmodule modPatch = ctx.LoadModulePTX("PatchProcessing.ptx");
Console.WriteLine("modPatch loaded");
CUmodule modBorder = ctx.LoadModulePTX("BorderTreatment.ptx");
Console.WriteLine("modBorder loaded");
CUmodule modError = ctx.LoadModulePTX("ErrorComputation.ptx");
Console.WriteLine("modError loaded");
CUmodule modPRelu = ctx.LoadModulePTX("PRelu.ptx");
Console.WriteLine("modPRelu loaded");
CUmodule modDeBayer = ctx.LoadModulePTX("DeBayer.ptx");
Console.WriteLine("all modules loaded");
deBayerGreenKernel = new DeBayerGreenKernel(modDeBayer, ctx);
deBayerRedBlueKernel = new DeBayerRedBlueKernel(modDeBayer, ctx);
//Both deBayer kernels are load from the same module: setting the constant variable for bayer pattern one is enough...
deBayerGreenKernel.BayerPattern = new BayerColor[] { BayerColor.Red, BayerColor.Green, BayerColor.Green, BayerColor.Blue };
prepareDataKernel = new PrepareDataKernel(modPatch, ctx);
restoreImageKernel = new RestoreImageKernel(modPatch, ctx);
Console.WriteLine("kernels loaded");
int countOwn = 468083;
int count5k = 33408;
string fileBase = @"/ssd/data/TrainingsDataNN/";
List <float3> WhiteBalanceFactors = new List <float3>();
FileStream fs1 = new FileStream(fileBase + "FromOwnDataset/WhiteBalancesOwn.txt", FileMode.Open, FileAccess.Read);
FileStream fs2 = new FileStream(fileBase + "From5kDataset/WhiteBalances5k.txt", FileMode.Open, FileAccess.Read);
StreamReader sr1 = new StreamReader(fs1);
StreamReader sr2 = new StreamReader(fs2);
for (int i = 0; i < countOwn; i++)
{
fileRawList.Add(fileBase + "FromOwnDataset/ISO" + ISO + "/img_" + i.ToString("0000000") + ".bin");
fileTrouthList.Add(fileBase + "FromOwnDataset/GroundTruth/img_" + i.ToString("0000000") + ".bin");
string line = sr1.ReadLine();
string[] values = line.Split('\t');
float3 wb = new float3(float.Parse(values[1], System.Globalization.NumberStyles.AllowDecimalPoint, CultureInfo.InvariantCulture),
float.Parse(values[2], System.Globalization.NumberStyles.AllowDecimalPoint, CultureInfo.InvariantCulture),
float.Parse(values[3], System.Globalization.NumberStyles.AllowDecimalPoint, CultureInfo.InvariantCulture));
WhiteBalanceFactors.Add(wb);
}
for (int i = 0; i < count5k; i++)
{
fileRawList.Add(fileBase + "From5kDataset/ISO" + ISO + "/img_" + i.ToString("0000000") + ".bin");
fileTrouthList.Add(fileBase + "From5kDataset/GroundTruth/img_" + i.ToString("0000000") + ".bin");
string line = sr2.ReadLine();
string[] values = line.Split('\t');
float3 wb = new float3(float.Parse(values[1], System.Globalization.NumberStyles.AllowDecimalPoint, CultureInfo.InvariantCulture),
float.Parse(values[2], System.Globalization.NumberStyles.AllowDecimalPoint, CultureInfo.InvariantCulture),
float.Parse(values[3], System.Globalization.NumberStyles.AllowDecimalPoint, CultureInfo.InvariantCulture));
WhiteBalanceFactors.Add(wb);
}
sr2.Close();
sr1.Close();
baOriginal = new float3[countOwn + count5k][];
baRAW = new float[countOwn + count5k][];
Random rand = new Random(0);
//random order for the image patches
for (int i = 0; i < countOwn + count5k - 1; i++)
{
int r = i + (rand.Next() % (countOwn + count5k - i));
string temp = fileRawList[i];
fileRawList[i] = fileRawList[r];
fileRawList[r] = temp;
temp = fileTrouthList[i];
fileTrouthList[i] = fileTrouthList[r];
fileTrouthList[r] = temp;
float3 tempf = WhiteBalanceFactors[i];
WhiteBalanceFactors[i] = WhiteBalanceFactors[r];
WhiteBalanceFactors[r] = tempf;
}
Console.WriteLine("Initialization done!");
int trainingSize = (int)((countOwn + count5k) * 0.9f); //4 patches per file
int testSize = fileRawList.Count - trainingSize;
CudaBlas blas = new CudaBlas(PointerMode.Host);
CudaDNNContext cudnn = new CudaDNNContext();
int patchSize = 31;
int patchSize4 = 66; //Size of an 2x2 patch read from file
int batch = 64;
float normalization = 0.5f;
//define neural network:
StartLayer start = new StartLayer(patchSize, patchSize, 3, batch);
FinalLayer final = new FinalLayer(patchSize, patchSize, 3, batch, FinalLayer.Norm.Mix, ctx, modError);
ConvolutionalLayer conv1 = new ConvolutionalLayer(patchSize, patchSize, 3, patchSize, patchSize, 64, batch, 9, 9, ConvolutionalLayer.Activation.PRelu, blas, cudnn, ctx, modBorder, modPRelu);
ConvolutionalLayer conv2 = new ConvolutionalLayer(patchSize, patchSize, 64, patchSize, patchSize, 64, batch, 5, 5, ConvolutionalLayer.Activation.PRelu, blas, cudnn, ctx, modBorder, modPRelu);
ConvolutionalLayer conv3 = new ConvolutionalLayer(patchSize, patchSize, 64, patchSize, patchSize, 3, batch, 5, 5, ConvolutionalLayer.Activation.None, blas, cudnn, ctx, modBorder, modPRelu);
start.ConnectFollowingLayer(conv1);
conv1.ConnectFollowingLayer(conv2);
conv2.ConnectFollowingLayer(conv3);
conv3.ConnectFollowingLayer(final);
CudaDeviceVariable <float3> imgA = new CudaDeviceVariable <float3>(patchSize4 * patchSize4);
CudaDeviceVariable <float3> imgB = new CudaDeviceVariable <float3>(patchSize4 * patchSize4);
CudaDeviceVariable <float> rawd = new CudaDeviceVariable <float>(patchSize4 * patchSize4);
CudaDeviceVariable <float> inputImgs = new CudaDeviceVariable <float>(patchSize * patchSize * 3 * batch);
CudaDeviceVariable <float> groundTrouth = new CudaDeviceVariable <float>(patchSize * patchSize * 3 * batch);
NPPImage_8uC3 imgU3a = new NPPImage_8uC3(patchSize, patchSize);
NPPImage_8uC3 imgU3b = new NPPImage_8uC3(patchSize, patchSize);
NPPImage_8uC3 imgU3c = new NPPImage_8uC3(patchSize, patchSize);
Bitmap a = new Bitmap(patchSize, patchSize, PixelFormat.Format24bppRgb);
Bitmap b = new Bitmap(patchSize, patchSize, PixelFormat.Format24bppRgb);
Bitmap c = new Bitmap(patchSize, patchSize, PixelFormat.Format24bppRgb);
Random randImageOutput = new Random(0);
Random randForInit = new Random(0);
start.InitRandomWeight(randForInit);
conv1.SetActivation(0.1f);
conv2.SetActivation(0.1f);
int startEpoch = warmStart;
FileStream fs;
//restore network in case of warm start:
if (warmStart > 0)
{
fs = new FileStream("epoch_" + learning_rate.ToString(CultureInfo.InvariantCulture) + "_" + ISO + "_" + (warmStart - 1) + ".cnn", FileMode.Open, FileAccess.Read);
start.RestoreValues(fs);
fs.Close();
fs.Dispose();
}
//validate results on validation data set
if (crosscheck)
{
FileStream csvResult = new FileStream("results_" + learning_rate.ToString(CultureInfo.InvariantCulture) + "_" + ISO + ".csv", FileMode.Append, FileAccess.Write);
StreamWriter sw = new StreamWriter(csvResult);
sw.WriteLine("L1;L2;Mix;Filename");
for (int i = 0; i < 2000; i += 1)
{
string filename = "epoch_" + learning_rate.ToString(CultureInfo.InvariantCulture) + "_" + ISO + "_" + i + ".cnn";
try
{
FileStream cnn = new FileStream(filename, FileMode.Open, FileAccess.Read);
start.RestoreValues(cnn);
cnn.Close();
cnn.Dispose();
}
catch (Exception)
{
Console.WriteLine("Skipping: " + i);
continue;
}
double errorL1 = 0;
double errorL2 = 0;
double errorMix = 0;
for (int iter = 0; iter < testSize / batch * 4; iter++)
{
//Prepare batch for training:
for (int ba = 0; ba < batch / 4; ba++)
{
int idx = iter * (batch / 4) + ba + trainingSize;
float3[] original;
float[] raw;
if (baRAW[idx - trainingSize] == null)
{
original = ReadRAWFloat3(fileTrouthList[idx]);
raw = ReadRAWFloat(fileRawList[idx]);
baOriginal[idx - trainingSize] = original;
baRAW[idx - trainingSize] = raw;
}
else
{
original = baOriginal[idx - trainingSize];
raw = baRAW[idx - trainingSize];
}
rawd.CopyToDevice(raw);
imgA.CopyToDevice(original);
deBayerGreenKernel.RunSafe(rawd, imgB, patchSize4, new float3(0, 0, 0), WhiteBalanceFactors[idx]);
deBayerRedBlueKernel.RunSafe(rawd, imgB, patchSize4, new float3(0, 0, 0), WhiteBalanceFactors[idx]);
prepareDataKernel.RunSafe(imgA, imgB, groundTrouth, inputImgs, ba, normalization, WhiteBalanceFactors[idx]);
}
start.SetData(inputImgs);
final.SetGroundTrouth(groundTrouth);
float err = start.InferenceTraining(inputImgs);
errorMix += err;
errorL1 += final.GetError(FinalLayer.Norm.L1);
errorL2 += final.GetError(FinalLayer.Norm.L2);
}
Console.WriteLine("Results for: " + filename);
Console.WriteLine("Mean Error L1: " + errorL1 / testSize * batch / 4);
Console.WriteLine("Mean Error L2: " + errorL2 / testSize * batch / 4);
Console.WriteLine("Mean Error Mix: " + errorMix / testSize * batch / 4);
sw.Write((errorL1 / testSize * batch / 4).ToString().Replace(".", ","));
sw.Write(";");
sw.Write((errorL2 / testSize * batch / 4).ToString().Replace(".", ","));
sw.Write(";");
sw.Write((errorMix / testSize * batch / 4).ToString().Replace(".", ","));
sw.Write(";");
sw.WriteLine(filename);
sw.Flush();
}
sw.Close();
csvResult.Close();
csvResult.Dispose();
}
//or train existing network:
else
{
double error = 0;
double errorEpoch = 0;
for (int epoch = startEpoch; epoch < 2000; epoch++)
{
errorEpoch = 0;
error = 0;
for (int iter = 0; iter < trainingSize / batch * 4; iter++)
{
//Prepare batch for training:
for (int ba = 0; ba < batch / 4; ba++)
{
int idx = iter * (batch / 4) + ba;
float3[] original;
float[] raw;
if (baRAW[idx] == null)
{
original = ReadRAWFloat3(fileTrouthList[idx]);
raw = ReadRAWFloat(fileRawList[idx]);
baOriginal[idx] = original;
baRAW[idx] = raw;
}
else
{
original = baOriginal[idx];
raw = baRAW[idx];
}
rawd.CopyToDevice(raw);
imgA.CopyToDevice(original);
deBayerGreenKernel.RunSafe(rawd, imgB, patchSize4, new float3(0, 0, 0), WhiteBalanceFactors[idx]);
deBayerRedBlueKernel.RunSafe(rawd, imgB, patchSize4, new float3(0, 0, 0), WhiteBalanceFactors[idx]);
prepareDataKernel.RunSafe(imgA, imgB, groundTrouth, inputImgs, ba, normalization, WhiteBalanceFactors[idx]);
}
start.SetData(inputImgs);
final.SetGroundTrouth(groundTrouth);
float err = start.InferenceTraining(inputImgs);
final.BackPropagation(groundTrouth);
start.UpdateWeights(GetLearningRate(epoch * (trainingSize) / batch * 4 + iter));//*0+951342
error += err;
errorEpoch += err;
if ((epoch * trainingSize / batch * 4 + iter) % 1000 == 0 && iter != 0)
{
FileStream status = new FileStream("status_" + learning_rate.ToString(CultureInfo.InvariantCulture) + "_" + ISO + ".csv", FileMode.Append, FileAccess.Write);
StreamWriter sw = new StreamWriter(status);
sw.WriteLine((error / 1000.0).ToString().Replace(".", ",") + ";" + GetLearningRate(epoch * trainingSize / batch * 4 + iter).ToString().Replace(".", ","));
sw.Close();
status.Close();
status.Dispose();
error = 0;
}
//if ((epoch * trainingSize / batch * 4 + iter) % 10000 == 0)
//{
// fs = new FileStream("iter_" + learning_rate.ToString(CultureInfo.InvariantCulture) + "_" + ISO + "_" + (epoch * trainingSize / batch * 4 + iter) + ".cnn", FileMode.Create, FileAccess.Write);
// start.SaveValues(fs);
// fs.Close();
// fs.Dispose();
// Console.WriteLine("Network saved for iteration " + (epoch * trainingSize / batch * 4 + iter) + "!");
//}
Console.WriteLine("Epoch: " + epoch + " Iteration: " + (epoch * trainingSize / batch * 4 + iter) + ", Error: " + err);
if (saveImages && iter == 0)//(epoch * trainingSize / batch * 4 + iter) % 10000 == 0 &&
{
for (int i = 0; i < 1; i++)
{
int imgidx = randImageOutput.Next(batch);
float3 wb = WhiteBalanceFactors[iter * (batch / 4) + imgidx / 4];
restoreImageKernel.RunSafe(groundTrouth, imgU3a, imgidx, wb.x, wb.y, wb.z, normalization);
restoreImageKernel.RunSafe(inputImgs, imgU3b, imgidx, wb.x, wb.y, wb.z, normalization);
CudaDeviceVariable <float> res = final.GetResult();
restoreImageKernel.RunSafe(res, imgU3c, imgidx, wb.x, wb.y, wb.z, normalization);
imgU3a.CopyToHost(a);
imgU3b.CopyToHost(b);
imgU3c.CopyToHost(c);
a.Save("GroundTrouth_" + learning_rate.ToString(CultureInfo.InvariantCulture) + "_" + ISO + "_" + epoch + "_" + imgidx + ".png");// * trainingSize / batch * 4 + iter
b.Save("Input_" + learning_rate.ToString(CultureInfo.InvariantCulture) + "_" + ISO + "_" + epoch + "_" + imgidx + ".png");
c.Save("Result_" + learning_rate.ToString(CultureInfo.InvariantCulture) + "_" + ISO + "_" + epoch + "_" + imgidx + ".png");
}
}
}
errorEpoch /= trainingSize / batch * 4;
fs = new FileStream("errorEpoch_" + learning_rate.ToString(CultureInfo.InvariantCulture) + "_" + ISO + ".csv", FileMode.Append, FileAccess.Write);
StreamWriter sw2 = new StreamWriter(fs);
sw2.WriteLine(errorEpoch.ToString().Replace(".", ","));
sw2.Close();
fs.Close();
fs.Dispose();
fs = new FileStream("epoch_" + learning_rate.ToString(CultureInfo.InvariantCulture) + "_" + ISO + "_" + epoch + ".cnn", FileMode.Create, FileAccess.Write);
start.SaveValues(fs);
fs.Close();
fs.Dispose();
}
}
}
