paleo/third_party/libcudnn.py
def cudnnSetConvolution2dDescriptor(convDesc, pad_h, pad_w, u, v, upscalex,
                                    upscaley, mode):
    """"
    Initialize a convolution descriptor.

    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.

    Parameters
    ----------
    convDesc : cudnnConvolutionDescriptor
        Handle to a previously created convolution descriptor.
    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.
    uscaley : int
        Upscale the input in y-direction.
    mode : cudnnConvolutionMode
        Select between CUDNN_CONVOLUTION or CUDNN_CROSS_CORRELATION.
    """

    status = _libcudnn.cudnnSetConvolution2dDescriptor(
        convDesc, pad_h, pad_w, u, v, upscalex, upscaley, mode)
    cudnnCheckStatus(status)

    Initialize a convolution descriptor.

    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.

    Parameters
    ----------
    convDesc : cudnnConvolutionDescriptor
        Handle to a previously created convolution descriptor.
    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.
    uscaley : int
        Upscale the input in y-direction.
    mode : cudnnConvolutionMode
        Select between CUDNN_CONVOLUTION or CUDNN_CROSS_CORRELATION.
    
Initialize a convolution descriptor.
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.

Parameters

convDesc : cudnnConvolutionDescriptor
Handle to a previously created convolution descriptor.
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.
uscaley : int
Upscale the input in y-direction.
Initialize a convolution descriptor . 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 . Parameters convDesc : cudnnConvolutionDescriptor Handle to a previously created convolution descriptor . 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 . uscaley : int Upscale the input in y - direction .
def cudnnSetConvolution2dDescriptor(convDesc, pad_h, pad_w, u, v, upscalex,
                                    upscaley, mode):
    status = _libcudnn.cudnnSetConvolution2dDescriptor(
        convDesc, pad_h, pad_w, u, v, upscalex, upscaley, mode)
    cudnnCheckStatus(status)
def cudnnSetConvolution2dDescriptor ( convDesc , pad_h , pad_w , u , v , upscalex , upscaley , mode ) : status = _libcudnn . cudnnSetConvolution2dDescriptor ( convDesc , pad_h , pad_w , u , v , upscalex , upscaley , mode ) cudnnCheckStatus ( status )
Initialize a convolution descriptor.
Initialize a convolution descriptor .
""""
    Initialize a convolution descriptor.

    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.

    Parameters
    ----------
    convDesc : cudnnConvolutionDescriptor
        Handle to a previously created convolution descriptor.
    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.
    uscaley : int
        Upscale the input in y-direction.
    mode : cudnnConvolutionMode
        Select between CUDNN_CONVOLUTION or CUDNN_CROSS_CORRELATION.
    """
convDesc
pad_h
pad_w
u
v
upscalex
upscaley
mode