def test_isinf():
b = 1.0 * numpy.random.randint(0, 2, (2, 3))
b[b == 0] = numpy.inf
a = afnumpy.array(b)
fassert(afnumpy.isnan(a), numpy.isnan(b))
def gpuSIRT(tomo, angles, center, input_params):
print('Starting GPU SIRT recon')
#allocate space for final answer
af.set_device(
input_params['gpu_device']) #Set the device number for gpu based code
#Change tomopy format
new_tomo = np.transpose(tomo, (1, 2, 0)) #slice, columns, angles
im_size = new_tomo.shape[1]
num_slice = new_tomo.shape[0]
num_angles = new_tomo.shape[2]
pad_size = np.int16(im_size * input_params['oversamp_factor'])
nufft_scaling = (np.pi / pad_size)**2
num_iter = input_params['num_iter']
#Initialize structures for NUFFT
sino = {}
geom = {}
sino['Ns'] = pad_size #Sinogram size after padding
sino['Ns_orig'] = im_size #size of original sinogram
sino['center'] = center + (sino['Ns'] / 2 - sino['Ns_orig'] / 2
) #for padded sinogram
sino['angles'] = angles
#Initialize NUFFT parameters
nufft_params = init_nufft_params(sino, geom)
temp_y = afnp.zeros((sino['Ns'], num_angles), dtype=afnp.complex64)
temp_x = afnp.zeros((sino['Ns'], sino['Ns']), dtype=afnp.complex64)
x_recon = afnp.zeros((num_slice / 2, sino['Ns_orig'], sino['Ns_orig']),
dtype=afnp.complex64)
pad_idx = slice(sino['Ns'] / 2 - sino['Ns_orig'] / 2,
sino['Ns'] / 2 + sino['Ns_orig'] / 2)
#allocate output array
rec_sirt_final = np.zeros((num_slice, sino['Ns_orig'], sino['Ns_orig']),
dtype=np.float32)
#Pre-compute diagonal scaling matrices ; one the same size as the image and the other the same as data
#initialize an image of all ones
x_ones = afnp.ones((sino['Ns_orig'], sino['Ns_orig']),
dtype=afnp.complex64)
temp_x[pad_idx, pad_idx] = x_ones
temp_proj = forward_project(temp_x,
nufft_params) * (sino['Ns'] * afnp.pi / 2)
R = 1 / afnp.abs(temp_proj)
R[afnp.isnan(R)] = 0
R[afnp.isinf(R)] = 0
R = afnp.array(R, dtype=afnp.complex64)
#Initialize a sinogram of all ones
y_ones = afnp.ones((sino['Ns_orig'], num_angles), dtype=afnp.complex64)
temp_y[pad_idx] = y_ones
temp_backproj = back_project(temp_y, nufft_params) * nufft_scaling / 2
C = 1 / (afnp.abs(temp_backproj))
C[afnp.isnan(C)] = 0
C[afnp.isinf(C)] = 0
C = afnp.array(C, dtype=afnp.complex64)
#Move all data to GPU
slice_1 = slice(0, num_slice, 2)
slice_2 = slice(1, num_slice, 2)
gdata = afnp.array(new_tomo[slice_1] + 1j * new_tomo[slice_2],
dtype=afnp.complex64)
#loop over all slices
for i in range(num_slice / 2):
for iter_num in range(num_iter):
#filtered back-projection
temp_x[pad_idx, pad_idx] = x_recon[i]
Ax = (np.pi / 2) * sino['Ns'] * forward_project(
temp_x, nufft_params)
temp_y[pad_idx] = gdata[i]
x_recon[i] = x_recon[i] + (
C * back_project(R * (temp_y - Ax), nufft_params) *
nufft_scaling / 2)[pad_idx, pad_idx]
#Move to CPU
#Rescale result to match tomopy
rec_sirt = np.array(x_recon, dtype=np.complex64)
rec_sirt_final[slice_1] = np.array(rec_sirt.real, dtype=np.float32)
rec_sirt_final[slice_2] = np.array(rec_sirt.imag, dtype=np.float32)
return rec_sirt_final
def test_isinf():
b = 1.0*numpy.random.randint(0,2,(2,3))
b[b == 0] = numpy.inf
a = afnumpy.array(b)
fassert(afnumpy.isnan(a), numpy.isnan(b))