def _fftconv(a, b, axes=(0, 1)):
"""Patched version of :func:`sporco.linalg.fftconv`."""
if cp.isrealobj(a) and cp.isrealobj(b):
fft = cp.fft.rfftn
ifft = cp.fft.irfftn
else:
fft = cp.fft.fftn
ifft = cp.fft.ifftn
dims = cp.maximum(cp.asarray([a.shape[i] for i in axes]),
cp.asarray([b.shape[i] for i in axes]))
dims = [int(d) for d in dims]
af = fft(a, dims, axes)
bf = fft(b, dims, axes)
return ifft(af * bf, dims, axes)
def _fftconv(a, b, axes=(0, 1)):
"""Patched version of :func:`sporco.linalg.fftconv`."""
if cp.isrealobj(a) and cp.isrealobj(b):
fft = cp.fft.rfftn
ifft = cp.fft.irfftn
else:
fft = cp.fft.fftn
ifft = cp.fft.ifftn
dims = cp.maximum(cp.asarray([a.shape[i] for i in axes]),
cp.asarray([b.shape[i] for i in axes]))
dims = [int(d) for d in dims]
af = fft(a, dims, axes)
bf = fft(b, dims, axes)
return ifft(af * bf, dims, axes)
def adj_lam(self, data, theta):
"""Adjoint Laminography transform (L^*)"""
res = cp.zeros([self.n2, self.n1, self.n0], dtype='complex64')
data_gpu = cp.asarray(data.astype('complex64'))
theta_gpu = cp.asarray(theta)
# C++ wrapper, send pointers to GPU arrays
self.adj(res.data.ptr, data_gpu.data.ptr, theta_gpu.data.ptr)
if (cp.isrealobj(data)):
res = res.real
if (isinstance(data, np.ndarray)):
res = res.get()
return res