pytomography.callbacks#

It’s often the case that you want to evaluate various metrics of a reconstructed object throughout iterations of a reconstruction algorithm. For example, you may want to look at the variance of radioactivity distribution in the liver as a function of iteration number in the OSEM algorithm. This is what callbacks can be used for. A callback is simply a function that takes in an object and returns some sort of metric. Callbacks are optional input to reconstruction algorithms; the run method of a callback is called after each subiteration of an iterative reconstruction algorithm. All user-defined callbacks should inherit from the base class CallBack. A subclass of this class could be used to compute noise-bias curves provided the __init__ method was redefined to take in some ground truth, and the run method was redefined to compare the obj to the ground truth.

Submodules#

Package Contents#

Classes#

CallBack

Abstract class used for callbacks. Subclasses must redefine the __init__ and run methods. If a callback is used as an argument in an iterative reconstruction algorihtm, the __run__ method is called after each subiteration.

class pytomography.callbacks.CallBack#

Abstract class used for callbacks. Subclasses must redefine the __init__ and run methods. If a callback is used as an argument in an iterative reconstruction algorihtm, the __run__ method is called after each subiteration.

abstract run(obj)#

Abstract method for run.

Parameters:

obj (torch.tensor[batch_size, Lx, Ly, Lz]) – An object which one can compute various statistics from.