C# Class ManagedCuda.CudaDNN.ConvolutionDescriptor

An opaque structure holding the description of a convolution operation.

Inheritance: IDisposable

Show file Open project: kunzmi/managedCuda Class Usage Examples

Public Methods

Method	Description
ConvolutionDescriptor ( ) : System
Dispose ( ) : void	Dispose
GetConvolution2dDescriptor ( int &pad_h, int &pad_w, int &u, int &v, int &upscalex, int &upscaley, cudnnConvolutionMode &mode, cudnnDataType &dataType ) : void	This function queries a previously initialized 2D convolution descriptor object.
GetConvolution2dForwardOutputDim ( TensorDescriptor inputTensorDesc, FilterDescriptor filterDesc, int &n, int &c, int &h, int &w ) : void	This function returns the dimensions of the resulting 4D tensor of a 2D convolution, given the convolution descriptor, the input tensor descriptor and the filter descriptor This function can help to setup the output tensor and allocate the proper amount of memory prior to launch the actual convolution. Each dimension h and w of the output images is computed as followed: outputDim = 1 + (inputDim + 2*pad - filterDim)/convolutionStride;
GetConvolutionNdDescriptor ( int arrayLengthRequested, int &arrayLength, int padA, int strideA, int upscaleA, cudnnConvolutionMode &mode, cudnnDataType &dataType ) : void	This function queries a previously initialized convolution descriptor object.
GetConvolutionNdForwardOutputDim ( TensorDescriptor inputTensorDesc, FilterDescriptor filterDesc, int nbDims, int tensorOuputDimA ) : void	This function returns the dimensions of the resulting n-D tensor of a nbDims-2-D convolution, given the convolution descriptor, the input tensor descriptor and the filter descriptor This function can help to setup the output tensor and allocate the proper amount of memory prior to launch the actual convolution. Each dimension of the (nbDims-2)-D images of the output tensor is computed as followed: outputDim = 1 + (inputDim + 2*pad - filterDim)/convolutionStride;
SetConvolution2dDescriptor ( int pad_h, int pad_w, int u, int v, int upscalex, int upscaley, cudnnConvolutionMode mode, cudnnDataType dataType ) : void	This function initializes a previously created convolution descriptor object into a 2D correlation. This function assumes that the tensor and filter descriptors corresponds to the formard convolution path and checks if their settings are valid. That same convolution descriptor can be reused in the backward path provided it corresponds to the same layer.
SetConvolutionNdDescriptor ( int arrayLength, int padA, int filterStrideA, int upscaleA, cudnnConvolutionMode mode, cudnnDataType dataType ) : void	This function initializes a previously created generic convolution descriptor object into a n-D correlation. That same convolution descriptor can be reused in the backward path provided it corresponds to the same layer. The convolution computation will done in the specified dataType, which can be potentially different from the input/output tensors.

Protected Methods

Method	Description
Dispose ( bool fDisposing ) : void	For IDisposable

Method Details

ConvolutionDescriptor() public method

public ConvolutionDescriptor ( ) : System
return	System

Dispose() public method

Dispose

public Dispose ( ) : void
return	void

Dispose() protected method

For IDisposable

protected Dispose ( bool fDisposing ) : void
fDisposing	bool
return	void

GetConvolution2dDescriptor() public method

This function queries a previously initialized 2D convolution descriptor object.

public GetConvolution2dDescriptor ( int &pad_h, int &pad_w, int &u, int &v, int &upscalex, int &upscaley, cudnnConvolutionMode &mode, cudnnDataType &dataType ) : void
pad_h	int	zero-padding height: number of rows of zeros implicitly concatenated /// onto the top and onto the bottom of input images.
pad_w	int	zero-padding width: number of columns of zeros implicitly concatenated /// onto the left and onto the right of input images.
u	int	Vertical filter stride.
v	int	Horizontal filter stride.
upscalex	int	Upscale the input in x-direction.
upscaley	int	Upscale the input in y-direction.
mode	cudnnConvolutionMode	convolution mode.
dataType	cudnnDataType	Selects the datatype in which the computation will be done.
return	void

GetConvolution2dForwardOutputDim() public method

This function returns the dimensions of the resulting 4D tensor of a 2D convolution, given the convolution descriptor, the input tensor descriptor and the filter descriptor This function can help to setup the output tensor and allocate the proper amount of memory prior to launch the actual convolution. Each dimension h and w of the output images is computed as followed: outputDim = 1 + (inputDim + 2*pad - filterDim)/convolutionStride;

public GetConvolution2dForwardOutputDim ( TensorDescriptor inputTensorDesc, FilterDescriptor filterDesc, int &n, int &c, int &h, int &w ) : void
inputTensorDesc	TensorDescriptor	Handle to a previously initialized tensor descriptor.
filterDesc	FilterDescriptor	Handle to a previously initialized filter descriptor.
n	int	Number of output images.
c	int	Number of output feature maps per image.
h	int	Height of each output feature map.
w	int	Width of each output feature map.
return	void

GetConvolutionNdDescriptor() public method

This function queries a previously initialized convolution descriptor object.

public GetConvolutionNdDescriptor ( int arrayLengthRequested, int &arrayLength, int padA, int strideA, int upscaleA, cudnnConvolutionMode &mode, cudnnDataType &dataType ) : void
arrayLengthRequested	int	Dimension of the expected convolution descriptor. It is also the /// minimum size of the arrays padA, filterStrideA and upsacleA in /// order to be able to hold the results
arrayLength	int	actual dimension of the convolution descriptor.
padA	int	Array of dimension of at least arrayLengthRequested that will be /// filled with the padding parameters from the provided convolution /// descriptor.
strideA	int	Array of dimension of at least arrayLengthRequested that will be /// filled with the filter stride from the provided convolution descriptor.
upscaleA	int	Array of dimension at least arrayLengthRequested that will be filled /// with the upscaling parameters from the provided convolution descriptor.
mode	cudnnConvolutionMode	convolution mode of the provided descriptor.
dataType	cudnnDataType	datatype of the provided descriptor.
return	void

GetConvolutionNdForwardOutputDim() public method

This function returns the dimensions of the resulting n-D tensor of a nbDims-2-D convolution, given the convolution descriptor, the input tensor descriptor and the filter descriptor This function can help to setup the output tensor and allocate the proper amount of memory prior to launch the actual convolution. Each dimension of the (nbDims-2)-D images of the output tensor is computed as followed: outputDim = 1 + (inputDim + 2*pad - filterDim)/convolutionStride;

public GetConvolutionNdForwardOutputDim ( TensorDescriptor inputTensorDesc, FilterDescriptor filterDesc, int nbDims, int tensorOuputDimA ) : void
inputTensorDesc	TensorDescriptor	Handle to a previously initialized tensor descriptor.
filterDesc	FilterDescriptor	Handle to a previously initialized filter descriptor.
nbDims	int	Dimension of the output tensor
tensorOuputDimA	int	Array of dimensions nbDims that contains on exit of this routine the sizes /// of the output tensor
return	void

SetConvolution2dDescriptor() public method

This function initializes a previously created convolution descriptor object into a 2D correlation. This function assumes that the tensor and filter descriptors corresponds to the formard convolution path and checks if their settings are valid. That same convolution descriptor can be reused in the backward path provided it corresponds to the same layer.

public SetConvolution2dDescriptor ( int pad_h, int pad_w, int u, int v, int upscalex, int upscaley, cudnnConvolutionMode mode, cudnnDataType dataType ) : void
pad_h	int	zero-padding height: number of rows of zeros implicitly concatenated /// onto the top and onto the bottom of input images.
pad_w	int	zero-padding width: number of columns of zeros implicitly concatenated /// onto the left and onto the right of input images.
u	int	Vertical filter stride.
v	int	Horizontal filter stride.
upscalex	int	Upscale the input in x-direction.
upscaley	int	Upscale the input in y-direction.
mode	cudnnConvolutionMode	Selects between CUDNN_CONVOLUTION and CUDNN_CROSS_CORRELATION.
dataType	cudnnDataType	Selects the datatype in which the computation will be done.
return	void

SetConvolutionNdDescriptor() public method

This function initializes a previously created generic convolution descriptor object into a n-D correlation. That same convolution descriptor can be reused in the backward path provided it corresponds to the same layer. The convolution computation will done in the specified dataType, which can be potentially different from the input/output tensors.

public SetConvolutionNdDescriptor ( int arrayLength, int padA, int filterStrideA, int upscaleA, cudnnConvolutionMode mode, cudnnDataType dataType ) : void
arrayLength	int	Dimension of the convolution.
padA	int	Array of dimension arrayLength containing the zero-padding size /// for each dimension. For every dimension, the padding represents the /// number of extra zeros implicitly concatenated at the start and at the /// end of every element of that dimension.
filterStrideA	int	Array of dimension arrayLength containing the filter stride for each /// dimension. For every dimension, the fitler stride represents the number /// of elements to slide to reach the next start of the filtering window of /// the next point.
upscaleA	int	Array of dimension arrayLength containing the upscale factor for each dimension.
mode	cudnnConvolutionMode	Selects between CUDNN_CONVOLUTION and CUDNN_CROSS_CORRELATION.
dataType	cudnnDataType	Selects the datatype in which the computation will be done.
return	void