def _check_nd_args(input, weights, mode, origin, wghts_name='filter weights'):
_util._check_mode(mode)
# Weights must always be less than 2 GiB
if weights.nbytes >= (1 << 31):
raise RuntimeError('weights must be 2 GiB or less, use FFTs instead')
weight_dims = [x for x in weights.shape if x != 0]
if len(weight_dims) != input.ndim:
raise RuntimeError('{} array has incorrect shape'.format(wghts_name))
origins = _util._fix_sequence_arg(origin, len(weight_dims), 'origin', int)
for origin, width in zip(origins, weight_dims):
_util._check_origin(origin, width)
return tuple(origins), _util._get_inttype(input)
def _convert_1d_args(ndim, weights, origin, axis):
if weights.ndim != 1 or weights.size < 1:
raise RuntimeError('incorrect filter size')
axis = internal._normalize_axis_index(axis, ndim)
w_shape = [1] * ndim
w_shape[axis] = weights.size
weights = weights.reshape(w_shape)
origins = [0] * ndim
origins[axis] = _util._check_origin(origin, weights.size)
return weights, tuple(origins)
