operator = odl.tomo.RayTransform(space, geometry, impl=astra_impl) #this operator to create the corresponding phantoms (with the full specified layer)
#operator is a function class (so simply calling the operator, will call out the function)
# Ensure operator has fixed operator norm for scale invariance
opnorm = odl.power_method_opnorm(operator)
operator = (1 / opnorm) * operator
# Create tensorflow layer from odl operator
print("\ngenerating operator tensorflow layer")
odl_op_layer = odl.contrib.tensorflow.as_tensorflow_layer(operator, 'RayTransform')
print("generating adjoint operator tensorflow layer")
odl_op_layer_adjoint = odl.contrib.tensorflow.as_tensorflow_layer(operator.adjoint, 'RayTransformAdjoint')
partial_op = partial.PartialRay(space, impl=astra_impl)
print("preparing the partial layer")
odl_op_partial_layer = partial.tensor_partial_layer(partial_op, 'PartialRayTransform')
odl_op_partial_layer_adjoint = partial.tensor_partial_layer(partial_op.adjoint, 'PartialRayTransformAdjoint')
# Retrieving the angle projections
angle_partition = uniform_partition(0, np.pi, n_angles)
projections_array = (np.array(angle_partition.grid.coord_vectors)).ravel()
selection_array = np.arange(n_angles)
n_data = 5
n_iter = 2
n_primal = 5
n_dual = 5
#operator is a function class (so simply calling the operator, will call out the function)
# Ensure operator has fixed operator norm for scale invariance
opnorm = odl.power_method_opnorm(operator)
operator = (1 / opnorm) * operator
# Create tensorflow layer from odl operator
print("\ngenerating operator tensorflow layer")
odl_op_layer = odl.contrib.tensorflow.as_tensorflow_layer(
operator, 'RayTransform')
print("generating adjoint operator tensorflow layer")
odl_op_layer_adjoint = odl.contrib.tensorflow.as_tensorflow_layer(
operator.adjoint, 'RayTransformAdjoint')
partial_op = partial.PartialRay(space, impl='astra_cuda')
print("preparing the partial layer")
odl_op_partial_layer = partial.tensor_partial_layer(partial_op,
'PartialRayTransform')
odl_op_partial_layer_adjoint = partial.tensor_partial_layer(
partial_op.adjoint, 'PartialRayTransformAdjoint')
# Retrieving the angle projections
angle_partition = uniform_partition(0, np.pi, n_angles)
projections_array = (np.array(angle_partition.grid.coord_vectors)).ravel()
selection_array = np.arange(n_angles)
n_data = 5
n_iter = 10
n_primal = 5