openai / consistencydecoder - Conditional Complexity

Intro

Conditional complexity (also called cyclomatic complexity) is a term used to measure the complexity of software. The term refers to the number of possible paths through a program function. A higher value ofter means higher maintenance and testing costs (infosecinstitute.com).
Conditional complexity is calculated by counting all conditions in the program that can affect the execution path (e.g. if statement, loops, switches, and/or operators, try and catch blocks...).
Conditional complexity is measured at the unit level (methods, functions...).
Units are classified in four categories based on the measured McCabe index: 1-5 (simple units), 6-10 (medium complex units), 11-25 (complex units), 26+ (very complex units).

Conditional Complexity Overall

Legend:

51+

26-50

11-25

6-10

1-5

Alternative Visuals

Conditional Complexity per Extension

51+

26-50

11-25

6-10

1-5

Conditional Complexity per Logical Component

primary logical decomposition

51+

26-50

11-25

6-10

1-5

Most Complex Units

Top 9 most complex units

Unit	# lines	McCabe index	# params
def _download() in consistencydecoder/__init__.py	39	8	2
def ldm_transform_latent() in consistencydecoder/__init__.py	12	4	2
def load_image() in consistencydecoder/__init__.py	18	3	3
def betas_for_alpha_bar() in consistencydecoder/__init__.py	7	2	3
def _extract_into_tensor() in consistencydecoder/__init__.py	4	1	3
def __init__() in consistencydecoder/__init__.py	22	1	3
def round_timesteps() in consistencydecoder/__init__.py	2	1	0
def __call__() in consistencydecoder/__init__.py	4	1	0
def save_image() in consistencydecoder/__init__.py	8	1	2