public override float Inference(CudaDeviceVariable <float> input)
{
_input = input;
NPPImage_32fC1 tempConv = new NPPImage_32fC1(_tempConvolution.DevicePointer, InWidth, InHeight, InWidth * sizeof(float));
for (int outLayer = 0; outLayer < OutChannels; outLayer++)
{
SizeT offsetOut = outLayer * OutWidth * OutHeight * sizeof(float);
CUdeviceptr ptrWithOffsetOut = _z.DevicePointer + offsetOut;
NPPImage_32fC1 imgOut = new NPPImage_32fC1(ptrWithOffsetOut, OutWidth, OutHeight, OutWidth * sizeof(float));
imgOut.Set(0);
for (int inLayer = 0; inLayer < InChannels; inLayer++)
{
SizeT offsetIn = inLayer * InWidth * InHeight * sizeof(float);
CUdeviceptr ptrWithOffsetIn = _input.DevicePointer + offsetIn;
NPPImage_32fC1 imgIn = new NPPImage_32fC1(ptrWithOffsetIn, InWidth, InHeight, InWidth * sizeof(float));
imgIn.SetRoi(_filterX / 2, _filterY / 2, InWidth - _filterX + 1, InHeight - _filterY + 1);
SizeT offsetFilter = (outLayer * InChannels * _filterX * _filterY + inLayer * _filterX * _filterY) * sizeof(float);
CudaDeviceVariable <float> filter = new CudaDeviceVariable <float>(_weights.DevicePointer + offsetFilter, false, _filterX * _filterY * sizeof(float));
imgIn.Filter(tempConv, filter, new NppiSize(_filterX, _filterY), new NppiPoint(_filterX / 2, _filterY / 2));
imgOut.Add(tempConv);
}
imgOut.Add(bHost[outLayer]);
}
switch (_activation)
{
case Activation.None:
_y.CopyToDevice(_z);
break;
case Activation.Relu:
//_aRelu is set to 0!
_KernelPReluForward.RunSafe(_z, _aRelu, _y, _outWidth * _outHeight, _outChannels, _batch);
break;
case Activation.PRelu:
_KernelPReluForward.RunSafe(_z, _aRelu, _y, _outWidth * _outHeight, _outChannels, _batch);
break;
case Activation.LeakyRelu:
_KernelPReluForward.RunSafe(_z, _aRelu, _y, _outWidth * _outHeight, _outChannels, _batch);
break;
default:
break;
}
return(_nextLayer.Inference(_y));
}
public override float InferenceTraining(CudaDeviceVariable <float> input)
{
_kernelAddBorder.RunSafe(input, _withBorderInput, _inChannels, _batch, _inWidth, _filterX / 2);
_input = _withBorderInput;
_cudnn.ConvolutionForward(alpha, _descDataIn, _input,
_descFilter, _weights, _descConv,
_algoFwd, _workspace, beta,
_descDataOut, _z);
_cudnn.AddTensor(alpha, _descBias, _bias, alpha, _descDataOut, _z);
switch (_activation)
{
case Activation.None:
_y.CopyToDevice(_z);
break;
case Activation.Relu:
_cudnn.ActivationForward(_descActivation, alpha, _descDataOut, _z, beta, _descDataOut, _y);
break;
case Activation.PRelu:
_KernelPReluForward.RunSafe(_z, _aRelu, _y, _outWidth * _outHeight, _outChannels, _batch);
break;
case Activation.LeakyRelu:
_KernelPReluForward.RunSafe(_z, _aRelu, _y, _outWidth * _outHeight, _outChannels, _batch);
break;
default:
break;
}
return(_nextLayer.InferenceTraining(_y));
}
