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#
Abstract class used for callbacks. Subclasses must redefine the |
- class pytomography.callbacks.CallBack#
Abstract class used for callbacks. Subclasses must redefine the
__init__
andrun
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.