def test_callback(self):
domain = odl.uniform_discr([0, 0], [1, 1], (1, 1))
op = ScalingOperator(domain, 0.5)
niter = 7
iters = []
callback = CallbackStore(iters)
r = LandweberReconstructor(op, domain.one(), niter, callback=callback)
r.reconstruct(domain.one())
self.assertEqual(len(iters), niter)
def test_callback_assignment(self):
domain = odl.uniform_discr([0, 0], [1, 1], (1, 1))
op = ScalingOperator(domain, 0.5)
callback = CallbackApply(lambda x: None)
r = LandweberReconstructor(op, domain.one(), 7, callback=callback)
self.assertEqual(r.callback, callback)
r.reconstruct(domain.one())
self.assertEqual(r.callback, callback)
callback2 = CallbackApply(lambda x: 1.)
r.reconstruct(domain.one(), callback=callback2)
self.assertEqual(r.callback, callback)
proj_data = ray_trafo(phantom)
observation = (proj_data + np.random.poisson(0.3, proj_data.shape)).asarray()
test_data = DataPairs(observation, ground_truth, name='shepp-logan + pois')
# %% task table and reconstructors
eval_tt = TaskTable()
fbp_reconstructor = FBPReconstructor(ray_trafo,
hyper_params={
'filter_type': 'Hann',
'frequency_scaling': 0.8
})
cg_reconstructor = CGReconstructor(ray_trafo, reco_space.zero(), 4)
gn_reconstructor = GaussNewtonReconstructor(ray_trafo, reco_space.zero(), 2)
lw_reconstructor = LandweberReconstructor(ray_trafo, reco_space.zero(), 8)
mlem_reconstructor = MLEMReconstructor(ray_trafo, 0.5 * reco_space.one(), 1)
reconstructors = [
fbp_reconstructor, cg_reconstructor, gn_reconstructor, lw_reconstructor,
mlem_reconstructor
]
options = {'save_iterates': True}
eval_tt.append_all_combinations(reconstructors=reconstructors,
test_data=[test_data],
options=options)
# %% run task table
results = eval_tt.run()
results.apply_measures([PSNR, SSIM])