|
private cudnnDivisiveNormalizationForward ( |
||
| handle | ||
| normDesc | ||
| mode | cudnnDivNormMode | |
| alpha | double | |
| srcDesc | ||
| srcData | ManagedCuda.BasicTypes.CUdeviceptr | |
| srcMeansData | ManagedCuda.BasicTypes.CUdeviceptr | |
| tempData | ManagedCuda.BasicTypes.CUdeviceptr | |
| tempData2 | ManagedCuda.BasicTypes.CUdeviceptr | |
| beta | double | |
| destDesc | ||
| destData | ManagedCuda.BasicTypes.CUdeviceptr | |
| return | cudnnStatus | |
public void cudnnDivisiveNormalizationForward(
cudnnDivNormMode mode,
double alpha,
cudnnTensorDescriptor srcDesc, // same desc for means, temp, temp2
CUdeviceptr srcData,
CUdeviceptr srcMeansData, // if NULL, means are assumed to be zero
CUdeviceptr tempData,
CUdeviceptr tempData2,
double beta,
cudnnTensorDescriptor destDesc,
CUdeviceptr destData)
{
res = CudaDNNNativeMethods.cudnnDivisiveNormalizationForward(_handle, _desc, mode, ref alpha, srcDesc, srcData, srcMeansData, tempData, tempData2, ref beta, destDesc, destData);
Debug.WriteLine(String.Format("{0:G}, {1}: {2}", DateTime.Now, "cudnnDivisiveNormalizationForward", res));
if (res != cudnnStatus.Success)
{
throw new CudaDNNException(res);
}
}
/// <summary>
/// This function performs the forward DivisiveNormalization layer computation.
/// </summary>
/// <param name="mode">DivisiveNormalization layer mode of operation. Currently only
/// CUDNN_DIVNORM_PRECOMPUTED_MEANS is implemented. Normalization
/// is performed using the means input tensor that is expected to be
/// precomputed by the user.</param>
/// <param name="alpha">Pointer to scaling factors (in host memory) used to blend the layer output
/// value with prior value in the destination tensor as follows: dstValue =
/// alpha[0]*resultValue + beta[0]*priorDstValue. Please refer to this section
/// for additional details.</param>
/// <param name="xDesc">Tensor descriptor objects for the input and output tensors. Note that
/// srcDesc is shared between srcData, srcMeansData, tempData, tempData2
/// tensors.</param>
/// <param name="x">Input tensor data pointer in device memory.</param>
/// <param name="means">Input means tensor data pointer in device memory. Note that this tensor
/// can be NULL (in that case it's values are assumed to be zero during the
/// computation). This tensor also doesn't have to contain means, these can
/// be any values, a frequently used variation is a result of convolution with a
/// normalized positive kernel (such as Gaussian).</param>
/// <param name="temp">Temporary tensors in device memory. These are used for computing
/// intermediate values during the forward pass. These tensors do not have
/// to be preserved as inputs from forward to the backward pass. Both use
/// srcDesc as a descriptor.</param>
/// <param name="temp2">Temporary tensors in device memory. These are used for computing
/// intermediate values during the forward pass. These tensors do not have
/// to be preserved as inputs from forward to the backward pass. Both use
/// srcDesc as a descriptor.</param>
/// <param name="beta">Pointer to scaling factors (in host memory) used to blend the layer output
/// value with prior value in the destination tensor as follows: dstValue =
/// alpha[0]*resultValue + beta[0]*priorDstValue. Please refer to this section
/// for additional details.</param>
/// <param name="yDesc">Tensor descriptor objects for the input and output tensors. Note that
/// srcDesc is shared between srcData, srcMeansData, tempData, tempData2
/// tensors.</param>
/// <param name="y">Pointer in device memory to a tensor for the result of the forward DivisiveNormalization pass.</param>
public void cudnnDivisiveNormalizationForward(
cudnnDivNormMode mode,
float alpha,
cudnnTensorDescriptor xDesc, // same desc for means, temp, temp2
CUdeviceptr x,
CUdeviceptr means, // if NULL, means are assumed to be zero
CUdeviceptr temp,
CUdeviceptr temp2,
float beta,
cudnnTensorDescriptor yDesc,
CUdeviceptr y)
{
res = CudaDNNNativeMethods.cudnnDivisiveNormalizationForward(_handle, _desc, mode, ref alpha, xDesc, x, means, temp, temp2, ref beta, yDesc, y);
Debug.Write("");//Line(String.Format("{0:G}, {1}: {2}", DateTime.Now, "cudnnDivisiveNormalizationForward", res));
if (res != cudnnStatus.Success)
{
throw new CudaDNNException(res);
}
}
