public DenoiseAndDemoisaic(int tileSize, CudaContext ctx, CUmodule mod, bool UseCUDNN)
{
_tileSize = tileSize;
start = new StartLayer(tileSize, tileSize, 3, 1);
final = new FinalLayer(tileSize - 16, tileSize - 16, 3, 1, ctx, mod);
if (UseCUDNN)
{
CudaDNNContext cuddn = new CudaDNNContext();
conv1 = new ConvolutionalLayer(tileSize, tileSize, 3, tileSize - 8, tileSize - 8, 64, 1, 9, 9, ConvolutionalLayer.Activation.PRelu, cuddn, ctx, mod);
conv2 = new ConvolutionalLayer(tileSize - 8, tileSize - 8, 64, tileSize - 12, tileSize - 12, 64, 1, 5, 5, ConvolutionalLayer.Activation.PRelu, cuddn, ctx, mod);
conv3 = new ConvolutionalLayer(tileSize - 12, tileSize - 12, 64, tileSize - 16, tileSize - 16, 3, 1, 5, 5, ConvolutionalLayer.Activation.None, cuddn, ctx, mod);
start.ConnectFollowingLayer(conv1);
conv1.ConnectFollowingLayer(conv2);
conv2.ConnectFollowingLayer(conv3);
conv3.ConnectFollowingLayer(final);
}
else
{
conv1NPP = new ConvolutionalLayerNPP(tileSize, tileSize, 3, tileSize - 8, tileSize - 8, 64, 1, 9, 9, ConvolutionalLayerNPP.Activation.PRelu, ctx, mod);
conv2NPP = new ConvolutionalLayerNPP(tileSize - 8, tileSize - 8, 64, tileSize - 12, tileSize - 12, 64, 1, 5, 5, ConvolutionalLayerNPP.Activation.PRelu, ctx, mod);
conv3NPP = new ConvolutionalLayerNPP(tileSize - 12, tileSize - 12, 64, tileSize - 16, tileSize - 16, 3, 1, 5, 5, ConvolutionalLayerNPP.Activation.None, ctx, mod);
start.ConnectFollowingLayer(conv1NPP);
conv1NPP.ConnectFollowingLayer(conv2NPP);
conv2NPP.ConnectFollowingLayer(conv3NPP);
conv3NPP.ConnectFollowingLayer(final);
}
tileAsPlanes = new CudaDeviceVariable <float>(tileSize * tileSize * 3);
tile = new NPPImage_32fC3(tileSize, tileSize);
}
static TensorOpGpu()
{
_CudaContext = new CudaContext(0, true);
_CudaBlasHandle = new CudaBlas();
_CudaStream = new CudaStream();
_CudnnContext = new CudaDNNContext();
_KernelLoader = new KernelLoader();
}
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();
}
}
}
public ConvolutionalLayer(int widthIn, int heightIn, int channelsIn, int widthOut, int heightOut, int channelsOut, int batch, int filterWidth, int filterHeight, Activation activation, CudaBlas blasCtx, CudaDNNContext cudnnCtx, CudaContext ctx, CUmodule moduleBorder, CUmodule modulePrelu)
: base(widthIn, heightIn, channelsIn, widthOut, heightOut, channelsOut, batch)
{
_activation = activation;
_filterX = filterWidth;
_filterY = filterHeight;
_weights = new CudaDeviceVariable <float>(filterWidth * filterHeight * channelsIn * channelsOut);
_d_weights = new CudaDeviceVariable <float>(filterWidth * filterHeight * channelsIn * channelsOut);
_bias = new CudaDeviceVariable <float>(channelsOut);
_d_bias = new CudaDeviceVariable <float>(channelsOut);
_dx = new CudaDeviceVariable <float>(widthIn * heightIn * channelsIn * batch);
_y = new CudaDeviceVariable <float>(widthOut * heightOut * channelsOut * batch);
_dy = new CudaDeviceVariable <float>(widthOut * heightOut * channelsOut * batch);
_z = new CudaDeviceVariable <float>(widthOut * heightOut * channelsOut * batch);
_ones = new CudaDeviceVariable <float>(batch);
_withBorderInput = new CudaDeviceVariable <float>((widthIn + filterWidth - 1) * (heightIn + filterHeight - 1) * channelsIn * batch);
_withBorderDx = new CudaDeviceVariable <float>((widthIn + filterWidth - 1) * (heightIn + filterHeight - 1) * channelsIn * batch);
_cudnn = cudnnCtx;
_blas = blasCtx;
_descActivation = new ActivationDescriptor();
_descActivation.SetActivationDescriptor(cudnnActivationMode.Relu, cudnnNanPropagation.NotPropagateNan, 0);
_descBias = new TensorDescriptor();
_descBias.SetTensor4dDescriptor(cudnnTensorFormat.NCHW, cudnnDataType.Float, 1, channelsOut, 1, 1);
_descDataInBorder = new TensorDescriptor();
_descDataIn = new TensorDescriptor();
_descDataIn.SetTensor4dDescriptor(cudnnTensorFormat.NCHW, cudnnDataType.Float, batch, channelsIn, heightIn + filterHeight - 1, widthIn + filterWidth - 1);
_descDataOut = new TensorDescriptor();
_descDataOut.SetTensor4dDescriptor(cudnnTensorFormat.NCHW, cudnnDataType.Float, batch, channelsOut, heightOut, widthOut);
_descFilter = new FilterDescriptor();
_descFilter.SetFilter4dDescriptor(cudnnDataType.Float, cudnnTensorFormat.NCHW, channelsOut, channelsIn, filterWidth, filterHeight);
_descConv = new ConvolutionDescriptor();
_descConvBorder = new ConvolutionDescriptor();
_descConv.SetConvolution2dDescriptor(0, 0, 1, 1, 1, 1, cudnnConvolutionMode.Convolution, cudnnDataType.Float);
int n = 0;
int c = 0;
int h = 0;
int w = 0;
_descConv.GetConvolution2dForwardOutputDim(_descDataIn, _descFilter, ref n, ref c, ref h, ref w);
_kernelAddBorder = new AddBorderKernel(moduleBorder, ctx);
_kernelAddBorder.BlockDimensions = new ManagedCuda.VectorTypes.dim3(widthIn + filterWidth - 1, (heightIn + filterHeight - 1) / 2 + 1, 1);
_kernelCropBorder = new CropBorderKernel(moduleBorder, ctx);
_kernelCropBorder.BlockDimensions = new ManagedCuda.VectorTypes.dim3(widthIn, heightIn / 2 + 1, 1);
if (_activation == Activation.PRelu || _activation == Activation.LeakyRelu)
{
_temp = new CudaDeviceVariable <float>(channelsOut * batch);
_aRelu = new CudaDeviceVariable <float>(channelsOut);
_dARelu = new CudaDeviceVariable <float>(channelsOut);
_KernelPReluForward = new PReluForwardKernel(modulePrelu, ctx);
_KernelPReluBackward = new PReluBackwardKernel(modulePrelu, ctx);
_KernelPReluBackward1 = new PReluBackward1Kernel(modulePrelu, ctx);
_KernelPReluBackward2 = new PReluBackward2Kernel(modulePrelu, ctx);
_KernelPReluForward.SetComputeSize((uint)widthOut * (uint)heightOut, (uint)channelsOut, (uint)batch);
_KernelPReluBackward.SetComputeSize((uint)channelsOut, 1, 1);
}
cudnnConvolutionFwdAlgoPerf[] algos =
_cudnn.FindConvolutionForwardAlgorithm(_descDataIn, _descFilter, _descConv, _descDataOut, 5);
cudnnConvolutionBwdDataAlgoPerf[] algos2 = _cudnn.FindConvolutionBackwardDataAlgorithm(_descFilter, _descDataOut, _descConv, _descDataIn, 5);
_algoFwd = _cudnn.GetConvolutionForwardAlgorithm(_descDataIn, _descFilter, _descConv,
_descDataOut, cudnnConvolutionFwdPreference.PreferFastest, 0);
SizeT sizeInBytes = 0, tmpsize = 0;
sizeInBytes = _cudnn.GetConvolutionForwardWorkspaceSize(_descDataIn, _descFilter,
_descConv, _descDataOut, _algoFwd);
_algoBwdFilter = _cudnn.GetConvolutionBackwardFilterAlgorithm(_descDataIn, _descDataOut, _descConv, _descFilter,
cudnnConvolutionBwdFilterPreference.PreferFastest, 0);
tmpsize = _cudnn.GetConvolutionBackwardFilterWorkspaceSize(_descDataIn, _descDataOut, _descConv, _descFilter, _algoBwdFilter);
sizeInBytes = Math.Max(sizeInBytes, tmpsize);
_algoBwdData = _cudnn.GetConvolutionBackwardDataAlgorithm(_descFilter, _descDataOut, _descConv, _descDataIn, cudnnConvolutionBwdDataPreference.PreferFastest, 0);
tmpsize = _cudnn.GetConvolutionBackwardDataWorkspaceSize(_descFilter, _descDataOut, _descConv, _descDataIn, _algoBwdData);
sizeInBytes = Math.Max(sizeInBytes, tmpsize);
if (sizeInBytes > 0)
{
_workspace = new CudaDeviceVariable <byte>(sizeInBytes);
}
else
{
_workspace = CudaDeviceVariable <byte> .Null;
}
}