openai / Video-Pre-Training - 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).

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Conditional Complexity Overall

There are 251 units with 1,994 lines of code in units (70.7% of code).

0 very complex units (0 lines of code)
0 complex units (0 lines of code)
6 medium complex units (206 lines of code)
13 simple units (303 lines of code)
232 very simple units (1,485 lines of code)

Legend:

51+

26-50

11-25

6-10

1-5

Alternative Visuals

3D view of all units

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 50 most complex units

Unit	# lines	McCabe index	# params
def data_loader_worker() in data_loader.py	53	22	3
def parse_dtype() in lib/torch_util.py	26	18	1
def factored_buttons_to_groups() in lib/action_mapping.py	16	13	3
def _infer_part() in lib/misc.py	38	13	5
def json_action_to_env_action() in run_inverse_dynamics_model.py	31	12	1
def __init__() in data_loader.py	42	12	6
def preproc_qkv() in lib/xf.py	25	10	4
def from_factored() in lib/action_mapping.py	27	10	2
def _env_action_to_agent() in agent.py	11	9	4
def behavioural_cloning_train() in behavioural_cloning.py	54	8	4
def validate_env() in agent.py	12	7	1
def main() in run_inverse_dynamics_model.py	58	7	6
def _precompute_to_factored() in lib/action_mapping.py	21	7	1
def make_action_head() in lib/action_head.py	12	7	3
def tree_multimap() in lib/tree_util.py	16	7	4
def prefix_multimap() in lib/tree_util.py	19	7	4
def forward() in lib/util.py	18	6	4
def __init__() in lib/action_mapping.py	16	6	3
def _parse_reshape_str() in lib/misc.py	14	6	2
def __init__() in agent.py	18	5	5
def recurrent_forward() in lib/util.py	15	5	5
def _preproc() in lib/xf.py	40	5	5
def _infer_question_mark() in lib/misc.py	17	5	2
def reshape_undo() in lib/misc.py	25	5	7
def forward() in lib/policy.py	20	5	4
def forward() in lib/action_head.py	10	5	3
def get_norm_entropy_from_cat_head() in lib/minecraft_util.py	16	5	4
def tree_map() in lib/tree_util.py	10	5	3
def walk_pytree() in lib/tree_util.py	11	5	4
def initial_state() in lib/util.py	5	4	2
def dict_to_numpy() in lib/actions.py	15	4	2
def preproc_qkv() in lib/xf.py	7	4	2
def act() in lib/xf.py	9	4	2
def fixed_point() in lib/misc.py	9	4	3
def _handle_ellipsis() in lib/misc.py	18	4	3
def __init__() in lib/policy.py	29	4	6
def NormedLinear() in lib/torch_util.py	12	4	4
def load_average_with_metadata() in lib/torch_util.py	11	4	2
def forward() in lib/action_head.py	10	4	3
def store_args() in lib/minecraft_util.py	18	4	1
def get_norm_cat_entropy() in lib/minecraft_util.py	13	4	4
def get_diag_guassian_entropy() in lib/minecraft_util.py	12	4	3
def __eq__() in lib/tree_util.py	5	4	2
def composite_images_with_alpha() in data_loader.py	7	3	5
def __next__() in data_loader.py	14	3	1
def get_module_log_keys_recursive() in lib/util.py	7	3	1
def forward() in lib/util.py	7	3	2
def get_norm() in lib/util.py	7	3	3
def residual() in lib/xf.py	23	3	3
def _is_separate() in lib/xf.py	9	3	2