/// <summary>
/// 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.
/// </summary>
/// <param name="arrayLength">Dimension of the convolution.</param>
/// <param name="padA">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.</param>
/// <param name="filterStrideA">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.</param>
/// <param name="dilationA">Array of dimension arrayLength containing the dilation factor for each dimension.</param>
/// <param name="mode">Selects between CUDNN_CONVOLUTION and CUDNN_CROSS_CORRELATION.</param>
/// <param name="computeType">Selects the datatype in which the computation will be done.</param>
public void SetConvolutionNdDescriptor(int arrayLength, /* nbDims-2 size */
int[] padA,
int[] filterStrideA,
int[] dilationA,
cudnnConvolutionMode mode, cudnnDataType computeType
)
{
res = CudaDNNNativeMethods.cudnnSetConvolutionNdDescriptor(_desc, arrayLength, padA, filterStrideA, dilationA, mode, computeType);
Debug.WriteLine(String.Format("{0:G}, {1}: {2}", DateTime.Now, "cudnnSetConvolutionNdDescriptor", res));
if (res != cudnnStatus.Success)
{
throw new CudaDNNException(res);
}
}
/// <summary>
/// This function queries a previously initialized convolution descriptor object.
/// </summary>
/// <param name="arrayLengthRequested">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</param>
/// <param name="arrayLength">actual dimension of the convolution descriptor.</param>
/// <param name="padA">Array of dimension of at least arrayLengthRequested that will be
/// filled with the padding parameters from the provided convolution
/// descriptor.</param>
/// <param name="strideA">Array of dimension of at least arrayLengthRequested that will be
/// filled with the filter stride from the provided convolution descriptor.</param>
/// <param name="dilationA">Array of dimension at least arrayLengthRequested that will be filled
/// with the dilation parameters from the provided convolution descriptor.</param>
/// <param name="mode">convolution mode of the provided descriptor.</param>
/// <param name="computeType">datatype of the provided descriptor.</param>
public void GetConvolutionNdDescriptor(int arrayLengthRequested,
ref int arrayLength,
int[] padA,
int[] strideA,
int[] dilationA,
ref cudnnConvolutionMode mode, ref cudnnDataType computeType
)
{
res = CudaDNNNativeMethods.cudnnGetConvolutionNdDescriptor(_desc, arrayLengthRequested, ref arrayLength, padA, strideA, dilationA, ref mode, ref computeType);
Debug.WriteLine(String.Format("{0:G}, {1}: {2}", DateTime.Now, "cudnnGetConvolutionNdDescriptor", res));
if (res != cudnnStatus.Success)
{
throw new CudaDNNException(res);
}
}
public void SetConvolution2dDescriptor(int pad_h, // zero-padding height
int pad_w, // zero-padding width
int u, // vertical filter stride
int v, // horizontal filter stride
int upscalex, // upscale the input in x-direction
int upscaley, // upscale the input in y-direction
cudnnConvolutionMode mode
)
{
res = CudaDNNNativeMethods.cudnnSetConvolution2dDescriptor(_desc, pad_h, pad_w, u, v, upscalex, upscaley, mode);
Debug.WriteLine(String.Format("{0:G}, {1}: {2}", DateTime.Now, "cudnnSetConvolution2dDescriptor", res));
if (res != cudnnStatus.Success)
{
throw new CudaDNNException(res);
}
}
/// <summary>
/// This function queries a previously initialized 2D convolution descriptor object.
/// </summary>
/// <param name="pad_h">zero-padding height: number of rows of zeros implicitly concatenated
/// onto the top and onto the bottom of input images.</param>
/// <param name="pad_w">zero-padding width: number of columns of zeros implicitly concatenated
/// onto the left and onto the right of input images.</param>
/// <param name="u">Vertical filter stride.</param>
/// <param name="v">Horizontal filter stride.</param>
/// <param name="dilation_h">Filter height dilation.</param>
/// <param name="dilation_w">Filter width dilation.</param>
/// <param name="mode">convolution mode.</param>
/// <param name="dataType">Selects the datatype in which the computation will be done.</param>
public void GetConvolution2dDescriptor(ref int pad_h, // zero-padding height
ref int pad_w, // zero-padding width
ref int u, // vertical filter stride
ref int v, // horizontal filter stride
ref int dilation_h, // filter dilation in the vertical dimension
ref int dilation_w, // filter dilation in the horizontal dimension
ref cudnnConvolutionMode mode,
ref cudnnDataType dataType
)
{
res = CudaDNNNativeMethods.cudnnGetConvolution2dDescriptor(_desc, ref pad_h, ref pad_w, ref u, ref v, ref dilation_h, ref dilation_w, ref mode, ref dataType);
Debug.WriteLine(String.Format("{0:G}, {1}: {2}", DateTime.Now, "cudnnGetConvolution2dDescriptor", res));
if (res != cudnnStatus.Success)
{
throw new CudaDNNException(res);
}
}
/// <summary>
/// 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.
/// </summary>
/// <param name="pad_h">zero-padding height: number of rows of zeros implicitly concatenated
/// onto the top and onto the bottom of input images.</param>
/// <param name="pad_w">zero-padding width: number of columns of zeros implicitly concatenated
/// onto the left and onto the right of input images.</param>
/// <param name="u">Vertical filter stride.</param>
/// <param name="v">Horizontal filter stride.</param>
/// <param name="dilation_h">Filter height dilation.</param>
/// <param name="dilation_w">Filter width dilation.</param>
/// <param name="mode">Selects between CUDNN_CONVOLUTION and CUDNN_CROSS_CORRELATION.</param>
/// <param name="dataType">Selects the datatype in which the computation will be done.</param>
public void SetConvolution2dDescriptor(int pad_h, // zero-padding height
int pad_w, // zero-padding width
int u, // vertical filter stride
int v, // horizontal filter stride
int dilation_h, // filter dilation in the vertical dimension
int dilation_w, // filter dilation in the horizontal dimension
cudnnConvolutionMode mode,
cudnnDataType dataType
)
{
res = CudaDNNNativeMethods.cudnnSetConvolution2dDescriptor(_desc, pad_h, pad_w, u, v, dilation_h, dilation_w, mode, dataType);
Debug.Write(""); //Line(String.Format("{0:G}, {1}: {2}", DateTime.Now, "cudnnSetConvolution2dDescriptor", res));
if (res != cudnnStatus.Success)
{
throw new CudaDNNException(res);
}
}
public void GetConvolution2dDescriptor(ref int pad_h, // zero-padding height
ref int pad_w, // zero-padding width
ref int u, // vertical filter stride
ref int v, // horizontal filter stride
ref int upscalex, // upscale the input in x-direction
ref int upscaley, // upscale the input in y-direction
ref cudnnConvolutionMode mode
)
{
res = CudaDNNNativeMethods.cudnnGetConvolution2dDescriptor(_desc, ref pad_h, ref pad_w, ref u, ref v, ref upscalex, ref upscaley, ref mode);
Debug.WriteLine(String.Format("{0:G}, {1}: {2}", DateTime.Now, "cudnnGetConvolution2dDescriptor", res));
if (res != cudnnStatus.Success) throw new CudaDNNException(res);
}
public void SetConvolutionNdDescriptor(int arrayLength, /* nbDims-2 size */
int[] padA,
int[] filterStrideA,
int[] upscaleA,
cudnnConvolutionMode mode
)
{
res = CudaDNNNativeMethods.cudnnSetConvolutionNdDescriptor(_desc, arrayLength, padA, filterStrideA, upscaleA, mode);
Debug.WriteLine(String.Format("{0:G}, {1}: {2}", DateTime.Now, "cudnnSetConvolutionNdDescriptor", res));
if (res != cudnnStatus.Success) throw new CudaDNNException(res);
}
public void GetConvolutionNdDescriptor(int arrayLengthRequested,
ref int arrayLength,
int[] padA,
int[] strideA,
int[] upscaleA,
ref cudnnConvolutionMode mode
)
{
res = CudaDNNNativeMethods.cudnnGetConvolutionNdDescriptor(_desc, arrayLengthRequested, ref arrayLength, padA, strideA, upscaleA, ref mode);
Debug.WriteLine(String.Format("{0:G}, {1}: {2}", DateTime.Now, "cudnnGetConvolutionNdDescriptor", res));
if (res != cudnnStatus.Success) throw new CudaDNNException(res);
}
/// <summary>
/// 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.
/// </summary>
/// <param name="pad_h">zero-padding height: number of rows of zeros implicitly concatenated
/// onto the top and onto the bottom of input images.</param>
/// <param name="pad_w">zero-padding width: number of columns of zeros implicitly concatenated
/// onto the left and onto the right of input images.</param>
/// <param name="u">Vertical filter stride.</param>
/// <param name="v">Horizontal filter stride.</param>
/// <param name="upscalex">Upscale the input in x-direction.</param>
/// <param name="upscaley">Upscale the input in y-direction.</param>
/// <param name="mode">Selects between CUDNN_CONVOLUTION and CUDNN_CROSS_CORRELATION.</param>
/// <param name="dataType">Selects the datatype in which the computation will be done.</param>
public void SetConvolution2dDescriptor(int pad_h, // zero-padding height
int pad_w, // zero-padding width
int u, // vertical filter stride
int v, // horizontal filter stride
int upscalex, // upscale the input in x-direction
int upscaley, // upscale the input in y-direction
cudnnConvolutionMode mode,
cudnnDataType dataType
)
{
res = CudaDNNNativeMethods.cudnnSetConvolution2dDescriptor(_desc, pad_h, pad_w, u, v, upscalex, upscaley, mode, dataType);
Debug.WriteLine(String.Format("{0:G}, {1}: {2}", DateTime.Now, "cudnnSetConvolution2dDescriptor", res));
if (res != cudnnStatus.Success) throw new CudaDNNException(res);
}
public static extern cudnnStatus cudnnGetConvolutionNdDescriptor(cudnnConvolutionDescriptor convDesc,
int arrayLengthRequested,
ref int arrayLength,
int[] padA,
int[] strideA,
int[] upscaleA,
ref cudnnConvolutionMode mode,
ref cudnnDataType dataType // convolution data type
);
public static extern cudnnStatus cudnnSetConvolutionNdDescriptor(cudnnConvolutionDescriptor convDesc,
int arrayLength, /* nbDims-2 size */
int[] padA,
int[] filterStrideA,
int[] upscaleA,
cudnnConvolutionMode mode,
cudnnDataType dataType // convolution data type
);
public static extern cudnnStatus cudnnGetConvolution2dDescriptor( cudnnConvolutionDescriptor convDesc, ref int pad_h, // zero-padding height ref int pad_w, // zero-padding width ref int u, // vertical filter stride ref int v, // horizontal filter stride ref int upscalex, // upscale the input in x-direction ref int upscaley, // upscale the input in y-direction ref cudnnConvolutionMode mode );
public static extern cudnnStatus cudnnSetConvolution2dDescriptor( cudnnConvolutionDescriptor convDesc,
int pad_h, // zero-padding height
int pad_w, // zero-padding width
int u, // vertical filter stride
int v, // horizontal filter stride
int upscalex, // upscale the input in x-direction
int upscaley, // upscale the input in y-direction
cudnnConvolutionMode mode,
cudnnDataType dataType
);