Class LRNDescriptor

Inheritance

System.Object

LRNDescriptor

Implements

System.IDisposable

Inherited Members

System.Object.Equals(System.Object)

System.Object.Equals(System.Object, System.Object)

System.Object.GetHashCode()

System.Object.GetType()

System.Object.MemberwiseClone()

System.Object.ReferenceEquals(System.Object, System.Object)

System.Object.ToString()

Namespace: ManagedCuda.CudaDNN

Assembly: CudaDNN.dll

Syntax

public class LRNDescriptor : IDisposable

Constructors

LRNDescriptor(CudaDNNContext)

Declaration

public LRNDescriptor(CudaDNNContext context)

Parameters

Type	Name	Description
CudaDNNContext	context

Properties

Desc

Returns the inner handle.

Declaration

public cudnnLRNDescriptor Desc { get; }

Property Value

Type	Description
cudnnLRNDescriptor

Methods

cudnnDivisiveNormalizationBackward(cudnnDivNormMode, Double, cudnnTensorDescriptor, CUdeviceptr, CUdeviceptr, CUdeviceptr, CUdeviceptr, CUdeviceptr, Double, cudnnTensorDescriptor, CUdeviceptr, CUdeviceptr)

This function performs the backward DivisiveNormalization layer computation.

Declaration

public void cudnnDivisiveNormalizationBackward(cudnnDivNormMode mode, double alpha, cudnnTensorDescriptor xDesc, CUdeviceptr x, CUdeviceptr means, CUdeviceptr dy, CUdeviceptr temp, CUdeviceptr temp2, double beta, cudnnTensorDescriptor dXdMeansDesc, CUdeviceptr dx, CUdeviceptr dMeans)

Parameters

Type	Name	Description
cudnnDivNormMode	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.
System.Double	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.
cudnnTensorDescriptor	xDesc	Tensor descriptor and pointers in device memory for the bottom layer's data and means. (Bottom layer is the earlier layer in the computation graph during inference). Note: the means tensor is expected to be precomputed by the user. It can also contain any valid values (not required to be actual means, and can be for instance a result of a convolution with a Gaussian kernel).
CUdeviceptr	x	Tensor descriptor and pointers in device memory for the bottom layer's data and means. (Bottom layer is the earlier layer in the computation graph during inference). Note: the means tensor is expected to be precomputed by the user. It can also contain any valid values (not required to be actual means, and can be for instance a result of a convolution with a Gaussian kernel).
CUdeviceptr	means	Tensor descriptor and pointers in device memory for the bottom layer's data and means. (Bottom layer is the earlier layer in the computation graph during inference). Note: the means tensor is expected to be precomputed by the user. It can also contain any valid values (not required to be actual means, and can be for instance a result of a convolution with a Gaussian kernel).
CUdeviceptr	dy	Tensor pointer in device memory for the top layer's cumulative loss differential data (error backpropagation). (Top layer is the later layer in the computation graph during inference).
CUdeviceptr	temp	Temporary tensors in device memory. These are used for computing intermediate values during the backward pass. These tensors do not have to be preserved from forward to backward pass. Both use srcDesc as a descriptor.
CUdeviceptr	temp2	Temporary tensors in device memory. These are used for computing intermediate values during the backward pass. These tensors do not have to be preserved from forward to backward pass. Both use srcDesc as a descriptor.
System.Double	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.
cudnnTensorDescriptor	dXdMeansDesc	Tensor descriptor for destDataDiff and destMeansDiff.
CUdeviceptr	dx	Tensor pointers (in device memory) for the bottom layer's resulting differentials (data and means). Both share the same descriptor.
CUdeviceptr	dMeans	Tensor pointers (in device memory) for the bottom layer's resulting differentials (data and means). Both share the same descriptor.

cudnnDivisiveNormalizationBackward(cudnnDivNormMode, Single, cudnnTensorDescriptor, CUdeviceptr, CUdeviceptr, CUdeviceptr, CUdeviceptr, CUdeviceptr, Single, cudnnTensorDescriptor, CUdeviceptr, CUdeviceptr)

This function performs the backward DivisiveNormalization layer computation.

Declaration

public void cudnnDivisiveNormalizationBackward(cudnnDivNormMode mode, float alpha, cudnnTensorDescriptor xDesc, CUdeviceptr x, CUdeviceptr means, CUdeviceptr dy, CUdeviceptr temp, CUdeviceptr temp2, float beta, cudnnTensorDescriptor dXdMeansDesc, CUdeviceptr dx, CUdeviceptr dMeans)

Parameters

Type	Name	Description
cudnnDivNormMode	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.
System.Single	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.
cudnnTensorDescriptor	xDesc	Tensor descriptor and pointers in device memory for the bottom layer's data and means. (Bottom layer is the earlier layer in the computation graph during inference). Note: the means tensor is expected to be precomputed by the user. It can also contain any valid values (not required to be actual means, and can be for instance a result of a convolution with a Gaussian kernel).
CUdeviceptr	x	Tensor descriptor and pointers in device memory for the bottom layer's data and means. (Bottom layer is the earlier layer in the computation graph during inference). Note: the means tensor is expected to be precomputed by the user. It can also contain any valid values (not required to be actual means, and can be for instance a result of a convolution with a Gaussian kernel).
CUdeviceptr	means	Tensor descriptor and pointers in device memory for the bottom layer's data and means. (Bottom layer is the earlier layer in the computation graph during inference). Note: the means tensor is expected to be precomputed by the user. It can also contain any valid values (not required to be actual means, and can be for instance a result of a convolution with a Gaussian kernel).
CUdeviceptr	dy	Tensor pointer in device memory for the top layer's cumulative loss differential data (error backpropagation). (Top layer is the later layer in the computation graph during inference).
CUdeviceptr	temp	Temporary tensors in device memory. These are used for computing intermediate values during the backward pass. These tensors do not have to be preserved from forward to backward pass. Both use srcDesc as a descriptor.
CUdeviceptr	temp2	Temporary tensors in device memory. These are used for computing intermediate values during the backward pass. These tensors do not have to be preserved from forward to backward pass. Both use srcDesc as a descriptor.
System.Single	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.
cudnnTensorDescriptor	dXdMeansDesc	Tensor descriptor for destDataDiff and destMeansDiff.
CUdeviceptr	dx	Tensor pointers (in device memory) for the bottom layer's resulting differentials (data and means). Both share the same descriptor.
CUdeviceptr	dMeans	Tensor pointers (in device memory) for the bottom layer's resulting differentials (data and means). Both share the same descriptor.

cudnnDivisiveNormalizationForward(cudnnDivNormMode, Double, cudnnTensorDescriptor, CUdeviceptr, CUdeviceptr, CUdeviceptr, CUdeviceptr, Double, cudnnTensorDescriptor, CUdeviceptr)

This function performs the forward DivisiveNormalization layer computation.

Declaration

public void cudnnDivisiveNormalizationForward(cudnnDivNormMode mode, double alpha, cudnnTensorDescriptor xDesc, CUdeviceptr x, CUdeviceptr means, CUdeviceptr temp, CUdeviceptr temp2, double beta, cudnnTensorDescriptor yDesc, CUdeviceptr y)

Parameters

Type	Name	Description
cudnnDivNormMode	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.
System.Double	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.
cudnnTensorDescriptor	xDesc	Tensor descriptor objects for the input and output tensors. Note that srcDesc is shared between srcData, srcMeansData, tempData, tempData2 tensors.
CUdeviceptr	x	Input tensor data pointer in device memory.
CUdeviceptr	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).
CUdeviceptr	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.
CUdeviceptr	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.
System.Double	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.
cudnnTensorDescriptor	yDesc	Tensor descriptor objects for the input and output tensors. Note that srcDesc is shared between srcData, srcMeansData, tempData, tempData2 tensors.
CUdeviceptr	y	Pointer in device memory to a tensor for the result of the forward DivisiveNormalization pass.

cudnnDivisiveNormalizationForward(cudnnDivNormMode, Single, cudnnTensorDescriptor, CUdeviceptr, CUdeviceptr, CUdeviceptr, CUdeviceptr, Single, cudnnTensorDescriptor, CUdeviceptr)

This function performs the forward DivisiveNormalization layer computation.

Declaration

public void cudnnDivisiveNormalizationForward(cudnnDivNormMode mode, float alpha, cudnnTensorDescriptor xDesc, CUdeviceptr x, CUdeviceptr means, CUdeviceptr temp, CUdeviceptr temp2, float beta, cudnnTensorDescriptor yDesc, CUdeviceptr y)

Parameters

Type	Name	Description
cudnnDivNormMode	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.
System.Single	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.
cudnnTensorDescriptor	xDesc	Tensor descriptor objects for the input and output tensors. Note that srcDesc is shared between srcData, srcMeansData, tempData, tempData2 tensors.
CUdeviceptr	x	Input tensor data pointer in device memory.
CUdeviceptr	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).
CUdeviceptr	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.
CUdeviceptr	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.
System.Single	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.
cudnnTensorDescriptor	yDesc	Tensor descriptor objects for the input and output tensors. Note that srcDesc is shared between srcData, srcMeansData, tempData, tempData2 tensors.
CUdeviceptr	y	Pointer in device memory to a tensor for the result of the forward DivisiveNormalization pass.

cudnnLRNCrossChannelBackward(cudnnLRNMode, ref Double, cudnnTensorDescriptor, CUdeviceptr, cudnnTensorDescriptor, CUdeviceptr, cudnnTensorDescriptor, CUdeviceptr, ref Double, cudnnTensorDescriptor, CUdeviceptr)

This function performs the backward LRN layer computation.

Declaration

public void cudnnLRNCrossChannelBackward(cudnnLRNMode lrnMode, ref double alpha, cudnnTensorDescriptor yDesc, CUdeviceptr y, cudnnTensorDescriptor dyDesc, CUdeviceptr dy, cudnnTensorDescriptor xDesc, CUdeviceptr x, ref double beta, cudnnTensorDescriptor dxDesc, CUdeviceptr dx)

Parameters

Type	Name	Description
cudnnLRNMode	lrnMode	LRN layer mode of operation. Currently only CUDNN_LRN_CROSS_CHANNEL_DIM1 is implemented. Normalization is performed along the tensor's dimA[1].
System.Double	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.
cudnnTensorDescriptor	yDesc	Tensor descriptor and pointer in device memory for the bottom layer's data. (Bottom layer is the earlier layer in the computation graph during inference).
CUdeviceptr	y	Tensor descriptor and pointer in device memory for the bottom layer's data. (Bottom layer is the earlier layer in the computation graph during inference).
cudnnTensorDescriptor	dyDesc	Tensor descriptor and pointer in device memory for the top layer's cumulative loss differential data (error backpropagation). (Top layer is the later layer in the computation graph during inference).
CUdeviceptr	dy	Tensor descriptor and pointer in device memory for the top layer's cumulative loss differential data (error backpropagation). (Top layer is the later layer in the computation graph during inference).
cudnnTensorDescriptor	xDesc	Tensor descriptor and pointer in device memory for the bottom layer's data. (Bottom layer is the earlier layer in the computation graph during inference). Note that these values are not modified during backpropagation.
CUdeviceptr	x	Tensor descriptor and pointer in device memory for the bottom layer's data. (Bottom layer is the earlier layer in the computation graph during inference). Note that these values are not modified during backpropagation.
System.Double	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.
cudnnTensorDescriptor	dxDesc	Tensor descriptor and pointer in device memory for the bottom layer's cumulative loss differential data (error backpropagation). (Bottom layer is the earlier layer in the computation graph during inference).
CUdeviceptr	dx	Tensor descriptor and pointer in device memory for the bottom layer's cumulative loss differential data (error backpropagation). (Bottom layer is the earlier layer in the computation graph during inference).

cudnnLRNCrossChannelBackward(cudnnLRNMode, ref Single, cudnnTensorDescriptor, CUdeviceptr, cudnnTensorDescriptor, CUdeviceptr, cudnnTensorDescriptor, CUdeviceptr, ref Single, cudnnTensorDescriptor, CUdeviceptr)

This function performs the backward LRN layer computation.

Declaration

public void cudnnLRNCrossChannelBackward(cudnnLRNMode lrnMode, ref float alpha, cudnnTensorDescriptor yDesc, CUdeviceptr y, cudnnTensorDescriptor dyDesc, CUdeviceptr dy, cudnnTensorDescriptor xDesc, CUdeviceptr x, ref float beta, cudnnTensorDescriptor dxDesc, CUdeviceptr dx)

Parameters

Type	Name	Description
cudnnLRNMode	lrnMode	LRN layer mode of operation. Currently only CUDNN_LRN_CROSS_CHANNEL_DIM1 is implemented. Normalization is performed along the tensor's dimA[1].
System.Single	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.
cudnnTensorDescriptor	yDesc	Tensor descriptor and pointer in device memory for the bottom layer's data. (Bottom layer is the earlier layer in the computation graph during inference).
CUdeviceptr	y	Tensor descriptor and pointer in device memory for the bottom layer's data. (Bottom layer is the earlier layer in the computation graph during inference).
cudnnTensorDescriptor	dyDesc	Tensor descriptor and pointer in device memory for the top layer's cumulative loss differential data (error backpropagation). (Top layer is the later layer in the computation graph during inference).
CUdeviceptr	dy	Tensor descriptor and pointer in device memory for the top layer's cumulative loss differential data (error backpropagation). (Top layer is the later layer in the computation graph during inference).
cudnnTensorDescriptor	xDesc	Tensor descriptor and pointer in device memory for the bottom layer's data. (Bottom layer is the earlier layer in the computation graph during inference). Note that these values are not modified during backpropagation.
CUdeviceptr	x	Tensor descriptor and pointer in device memory for the bottom layer's data. (Bottom layer is the earlier layer in the computation graph during inference). Note that these values are not modified during backpropagation.
System.Single	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.
cudnnTensorDescriptor	dxDesc	Tensor descriptor and pointer in device memory for the bottom layer's cumulative loss differential data (error backpropagation). (Bottom layer is the earlier layer in the computation graph during inference).
CUdeviceptr	dx	Tensor descriptor and pointer in device memory for the bottom layer's cumulative loss differential data (error backpropagation). (Bottom layer is the earlier layer in the computation graph during inference).

cudnnLRNCrossChannelForward(cudnnLRNMode, Double, cudnnTensorDescriptor, CUdeviceptr, Double, cudnnTensorDescriptor, CUdeviceptr)

This function performs the forward LRN layer computation.

Declaration

public void cudnnLRNCrossChannelForward(cudnnLRNMode lrnMode, double alpha, cudnnTensorDescriptor xDesc, CUdeviceptr x, double beta, cudnnTensorDescriptor yDesc, CUdeviceptr y)

Parameters

Type	Name	Description
cudnnLRNMode	lrnMode	LRN layer mode of operation. Currently only CUDNN_LRN_CROSS_CHANNEL_DIM1 is implemented. Normalization is performed along the tensor's dimA[1].
System.Double	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.
cudnnTensorDescriptor	xDesc	Tensor descriptor objects for the input and output tensors.
CUdeviceptr	x	Input tensor data pointer in device memory.
System.Double	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.
cudnnTensorDescriptor	yDesc	Tensor descriptor objects for the input and output tensors.
CUdeviceptr	y	Output tensor data pointer in device memory.

cudnnLRNCrossChannelForward(cudnnLRNMode, Single, cudnnTensorDescriptor, CUdeviceptr, Single, cudnnTensorDescriptor, CUdeviceptr)

This function performs the forward LRN layer computation.

Declaration

public void cudnnLRNCrossChannelForward(cudnnLRNMode lrnMode, float alpha, cudnnTensorDescriptor xDesc, CUdeviceptr x, float beta, cudnnTensorDescriptor yDesc, CUdeviceptr y)

Parameters

Type	Name	Description
cudnnLRNMode	lrnMode	LRN layer mode of operation. Currently only CUDNN_LRN_CROSS_CHANNEL_DIM1 is implemented. Normalization is performed along the tensor's dimA[1].
System.Single	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.
cudnnTensorDescriptor	xDesc	Tensor descriptor objects for the input and output tensors.
CUdeviceptr	x	Input tensor data pointer in device memory.
System.Single	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.
cudnnTensorDescriptor	yDesc	Tensor descriptor objects for the input and output tensors.
CUdeviceptr	y	Output tensor data pointer in device memory.

Dispose()

Dispose

Declaration

public void Dispose()

Dispose(Boolean)

For IDisposable

Declaration

protected virtual void Dispose(bool fDisposing)

Parameters

Type	Name	Description
System.Boolean	fDisposing

Finalize()

For dispose

Declaration

protected void Finalize()

GetLRNDescriptor(ref UInt32, ref Double, ref Double, ref Double)

This function retrieves values stored in the previously initialized LRN descriptor object.

Declaration

public void GetLRNDescriptor(ref uint lrnN, ref double lrnAlpha, ref double lrnBeta, ref double lrnK)

Parameters

Type	Name	Description
System.UInt32	lrnN	Pointers to receive values of parameters stored in the descriptor object. See cudnnSetLRNDescriptor for more details. Any of these pointers can be NULL (no value is returned for the corresponding parameter).
System.Double	lrnAlpha	Pointers to receive values of parameters stored in the descriptor object. See cudnnSetLRNDescriptor for more details. Any of these pointers can be NULL (no value is returned for the corresponding parameter).
System.Double	lrnBeta	Pointers to receive values of parameters stored in the descriptor object. See cudnnSetLRNDescriptor for more details. Any of these pointers can be NULL (no value is returned for the corresponding parameter).
System.Double	lrnK	Pointers to receive values of parameters stored in the descriptor object. See cudnnSetLRNDescriptor for more details. Any of these pointers can be NULL (no value is returned for the corresponding parameter).

SetLRNDescriptor(UInt32, Double, Double, Double)

This function initializes a previously created LRN descriptor object.

Declaration

public void SetLRNDescriptor(uint lrnN, double lrnAlpha, double lrnBeta, double lrnK)

Parameters

Type	Name	Description
System.UInt32	lrnN	Normalization window width in elements. LRN layer uses a window [center-lookBehind, center+lookAhead], where lookBehind = floor( (lrnN-1)/2 ), lookAhead = lrnN-lookBehind-1. So for n=10, the window is [k-4...k...k+5] with a total of 10 samples. For DivisiveNormalization layer the window has the same extents as above in all 'spatial' dimensions (dimA[2], dimA[3], dimA[4]). By default lrnN is set to 5 in cudnnCreateLRNDescriptor.
System.Double	lrnAlpha	Value of the alpha variance scaling parameter in the normalization formula. Inside the library code this value is divided by the window width for LRN and by (window width)^#spatialDimensions for DivisiveNormalization. By default this value is set to 1e-4 in cudnnCreateLRNDescriptor.
System.Double	lrnBeta	Value of the beta power parameter in the normalization formula. By default this value is set to 0.75 in cudnnCreateLRNDescriptor.
System.Double	lrnK	Value of the k parameter in normalization formula. By default this value is set to 2.0.

Implements

System.IDisposable