sinogram_id, sinogram = astra.create_sino3d_gpu(phantom, astra_proj_geom,
astra_vol_geom)
# Create a data object for the reconstruction
rec_id = astra.data3d.create('-vol', astra_vol_geom)
# Set up the parameters for a reconstruction algorithm using the CUDA backend
cfg = astra.astra_dict('CGLS3D_CUDA')
cfg['ReconstructionDataId'] = rec_id
cfg['ProjectionDataId'] = sinogram_id
cfg['ProjectorId'] = proj_id
# Create the algorithm object from the configuration structure
alg_id = astra.algorithm.create(cfg)
with timer('ASTRA Run'):
# Run the algorithm
astra.algorithm.run(alg_id, niter)
# Get the result
rec = astra.data3d.get(rec_id)
# Clean up.
astra.algorithm.delete(alg_id)
astra.data3d.delete(rec_id)
astra.data3d.delete(sinogram_id)
astra.projector3d.delete(proj_id)
# --- ODL ---
# Create ray transform
# Perform some benchmarks with rn
opt_spc = odl.rn(n)
simple_spc = SimpleRn(n)
x, y, z = np.random.rand(n), np.random.rand(n), np.random.rand(n)
ox, oy, oz = (opt_spc.element(x.copy()), opt_spc.element(y.copy()),
opt_spc.element(z.copy()))
sx, sy, sz = (simple_spc.element(x.copy()), simple_spc.element(y.copy()),
simple_spc.element(z.copy()))
if 'cuda' in odl.space.entry_points.tensor_space_impl_names():
cu_spc = odl.rn(n, impl='cuda')
cx, cy, cz = (cu_spc.element(x.copy()), cu_spc.element(y.copy()),
cu_spc.element(z.copy()))
print(" lincomb:")
with timer("SimpleRn"):
for _ in range(iterations):
simple_spc.lincomb(2.13, sx, 3.14, sy, out=sz)
print("result: {}".format(sz[1:5]))
with timer("odl numpy"):
for _ in range(iterations):
opt_spc.lincomb(2.13, ox, 3.14, oy, out=oz)
print("result: {}".format(oz[1:5]))
if 'cuda' in odl.space.entry_points.tensor_space_impl_names():
with timer("odl cuda"):
for _ in range(iterations):
cu_spc.lincomb(2.13, cx, 3.14, cy, out=cz)
print("result: {}".format(cz[1:5]))