def fbpunet_reconstructor(dataset='ellipses',
size_part=1.0,
pretrained=True,
name=None):
"""
:param dataset: Can be 'ellipses' or 'lodopab'
:param size_part: Can be one of: [0.001, 0.002, 0.005, 0.01, 0.02, 0.05,
0.1, 0.2, 0.5, 1.0]
:return: The FBP+UNet method trained on the specified dataset and size
"""
try:
standard_dataset = load_standard_dataset(dataset)
params = Params.load('{}_fbpunet_{}'.format(dataset, size_part))
if name is None:
name = 'FBP+UNet ({} $\%$)'.format(100 * size_part)
reconstructor = FBPUNetReconstructor(standard_dataset.ray_trafo,
hyper_params=params.dict,
name=name)
if pretrained:
load_weights(reconstructor,
'{}_fbpunet_{}'.format(dataset, size_part))
return reconstructor
except Exception as e:
raise Exception(
'The reconstructor does not exist for the selected data_size')
def iradonmap_reconstructor(dataset='ellipses',
size_part=1.0,
pretrained=True,
name=None):
"""
:param dataset: Can be 'ellipses' or 'lodopab'
:param size_part: Can be one of: [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1.0]
:return: The iRadonMap method trained on the specified dataset and size
"""
try:
standard_dataset = load_standard_dataset(dataset)
params = Params.load('{}_iradonmap_{}'.format(dataset, size_part))
if name is None:
name = 'iRadonMap ({} $\%$)'.format(100 * size_part)
coord_mat = None
try:
coord_mat = np.load(
os.path.join(BASE_DIR, 'reconstructors',
'{}_iradonmap_coord_mat.npy'.format(dataset)))
except FileNotFoundError:
pass
reconstructor = IRadonMapReconstructor(standard_dataset.ray_trafo,
hyper_params=params.dict,
name=name,
coord_mat=coord_mat)
if pretrained:
load_weights(reconstructor,
'{}_iradonmap_{}'.format(dataset, size_part))
return reconstructor
except Exception as e:
raise Exception(
'The reconstructor has not been trained with the selected data_size'
)
def learnedpd_dip_reconstructor(dataset='ellipses', size_part=1.0, name=None):
"""
:param dataset: Can be 'ellipses' or 'lodopab'
:return: The Deep Image Prior (DIP) method for the specified dataset
"""
try:
standard_dataset = load_standard_dataset(dataset)
params = Params.load('{}_learnedpd_{}'.format(dataset, size_part))
learned_pd = LearnedPDReconstructor(standard_dataset.ray_trafo,
hyper_params=params.dict,
name='Learned PD ({}%)'.format(
size_part * 100))
load_weights(learned_pd, '{}_learnedpd_{}'.format(dataset, size_part))
# load hyper-parameters and create reconstructor
if name is None:
name = 'Learned PD ({} $\%$) + DIP'.format(100 * size_part)
params = Params.load('{}_learnedpd_dip_{}'.format(dataset, size_part))
reconstructor = DeepImagePriorInitialReconstructor(
standard_dataset.ray_trafo,
ini_reco=learned_pd,
hyper_params=params.dict,
name=name)
return reconstructor
except Exception as e:
raise Exception('The reconstructor doesn\'t exist')
def learnedgd_reconstructor(dataset='ellipses',
size_part=1.0,
pretrained=True,
name=None):
"""
:param dataset: Can be 'ellipses' or 'lodopab'
:param size_part: Can be one of: [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1.0]
:return: The Learned Gradient Descent method trained on the specified dataset and size
"""
try:
standard_dataset = load_standard_dataset(dataset)
params = Params.load('{}_learnedgd_{}'.format(dataset, size_part))
if name is None:
name = 'Learned GD ({} $\%$)'.format(100 * size_part)
reconstructor = LearnedGDReconstructor(standard_dataset.ray_trafo,
hyper_params=params.dict,
name=name)
if pretrained:
load_weights(reconstructor,
'{}_learnedgd_{}'.format(dataset, size_part))
return reconstructor
except Exception as e:
raise Exception(
'The reconstructor has not been trained with the selected data_size'
)