paleo/third_party/libcudnn.py
def cudnnGetConvolution2dDescriptor(convDesc):
    """"
    Get a convolution descriptor.

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

    Parameters
    ----------
    convDesc : cudnnConvolutionDescriptor
        Handle to a previously created convolution descriptor.

    Returns
    -------
    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
        Either CUDNN_CONVOLUTION or CUDNN_CROSS_CORRELATION.
    """
    pad_h = ctypes.c_int()
    pad_w = ctypes.c_int()
    u = ctypes.c_int()
    v = ctypes.c_int()
    upscalex = ctypes.c_int()
    upscaley = ctypes.c_int()
    mode = ctypes.c_int()

    status = _libcudnn.cudnnGetConvolution2dDescriptor(
        convDesc, ctypes.byref(pad_h), ctypes.byref(pad_w), ctypes.byref(u),
        ctypes.byref(v), ctypes.byref(upscalex), ctypes.byref(upscaley),
        ctypes.byref(mode))

    cudnnCheckStatus(status)

    return pad_h.value, pad_w.value, u.value, v.value, upscalex.value, upscaley.value, mode.value

    Get a convolution descriptor.

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

    Parameters
    ----------
    convDesc : cudnnConvolutionDescriptor
        Handle to a previously created convolution descriptor.

    Returns
    -------
    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
        Either CUDNN_CONVOLUTION or CUDNN_CROSS_CORRELATION.
    
Get a convolution descriptor.
This function queries a previously initialized 2D convolution descriptor object.

Parameters

convDesc : cudnnConvolutionDescriptor
Handle to a previously created convolution descriptor.

Returns

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.
Get a convolution descriptor . This function queries a previously initialized 2D convolution descriptor object . Parameters convDesc : cudnnConvolutionDescriptor Handle to a previously created convolution descriptor . Returns 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 .
def cudnnGetConvolution2dDescriptor(convDesc):
    pad_h = ctypes.c_int()
    pad_w = ctypes.c_int()
    u = ctypes.c_int()
    v = ctypes.c_int()
    upscalex = ctypes.c_int()
    upscaley = ctypes.c_int()
    mode = ctypes.c_int()
    status = _libcudnn.cudnnGetConvolution2dDescriptor(
        convDesc, ctypes.byref(pad_h), ctypes.byref(pad_w), ctypes.byref(u),
        ctypes.byref(v), ctypes.byref(upscalex), ctypes.byref(upscaley),
        ctypes.byref(mode))
    cudnnCheckStatus(status)
    return pad_h.value, pad_w.value, u.value, v.value, upscalex.value, upscaley.value, mode.value
def cudnnGetConvolution2dDescriptor ( convDesc ) : pad_h = ctypes . c_int ( ) pad_w = ctypes . c_int ( ) u = ctypes . c_int ( ) v = ctypes . c_int ( ) upscalex = ctypes . c_int ( ) upscaley = ctypes . c_int ( ) mode = ctypes . c_int ( ) status = _libcudnn . cudnnGetConvolution2dDescriptor ( convDesc , ctypes . byref ( pad_h ) , ctypes . byref ( pad_w ) , ctypes . byref ( u ) , ctypes . byref ( v ) , ctypes . byref ( upscalex ) , ctypes . byref ( upscaley ) , ctypes . byref ( mode ) ) cudnnCheckStatus ( status ) return pad_h . value , pad_w . value , u . value , v . value , upscalex . value , upscaley . value , mode . value
Get a convolution descriptor.
Get a convolution descriptor .
""""
    Get a convolution descriptor.

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

    Parameters
    ----------
    convDesc : cudnnConvolutionDescriptor
        Handle to a previously created convolution descriptor.

    Returns
    -------
    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
        Either CUDNN_CONVOLUTION or CUDNN_CROSS_CORRELATION.
    """
convDesc