openai / consistency_models - 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).

Learn more...

Conditional Complexity Overall

There are 327 units with 3,352 lines of code in units (70.8% of code).

0 very complex units (0 lines of code)
1 complex units (155 lines of code)
4 medium complex units (275 lines of code)
17 simple units (391 lines of code)
305 very simple units (2,531 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 main() in scripts/ternary_search.py	155	31	0
def main() in scripts/image_sample.py	82	16	0
def main() in scripts/cm_train.py	101	13	0
def run_loop() in cm/train_util.py	24	13	1
def forward_backward() in cm/train_util.py	68	11	3
def run_loop() in cm/train_util.py	12	10	1
def save() in cm/train_util.py	35	10	1
def update_with_local_losses() in cm/resample.py	20	9	3
def ema_and_scales_fn() in cm/script_util.py	43	9	1
def _update_shapes() in evaluations/evaluator.py	15	9	1
def writekvs() in cm/logger.py	25	8	2
def configure() in cm/logger.py	24	8	4
def forward_backward() in cm/train_util.py	31	7	3
def dumpkvs() in cm/logger.py	20	7	1
def mpi_weighted_mean() in cm/logger.py	22	7	2
def _read_bytes() in evaluations/evaluator.py	15	7	3
def get_activations() in evaluations/th_evaluator.py	38	7	5
def save() in cm/train_util.py	21	6	1
def normal_kl() in cm/losses.py	18	6	4
def writekvs() in cm/logger.py	24	6	2
def make_output_format() in cm/logger.py	14	6	3
def get_weightings() in cm/karras_diffusion.py	14	6	3
def get_param_groups_and_shapes() in cm/fp16_util.py	11	5	1
def _load_and_sync_target_parameters() in cm/train_util.py	17	5	1
def _load_and_sync_teacher_parameters() in cm/train_util.py	17	5	1
def reset_training_for_progdist() in cm/train_util.py	25	5	1
def load_state_dict() in cm/dist_util.py	17	5	2
def _list_image_files_recursively() in cm/image_datasets.py	10	5	1
def __getitem__() in cm/image_datasets.py	17	5	2
def __init__() in cm/random_util.py	15	5	3
def manifold_radii() in evaluations/evaluator.py	27	5	2
def open_npz_array() in evaluations/evaluator.py	15	5	2
def master_params_to_model_params() in cm/fp16_util.py	6	4	2
def state_dict_to_master_params() in cm/fp16_util.py	10	4	3
def _load_and_sync_parameters() in cm/train_util.py	13	4	1
def _load_ema_parameters() in cm/train_util.py	13	4	2
def run_step() in cm/train_util.py	13	4	3
def conv_nd() in cm/nn.py	8	4	3
def avg_pool_nd() in cm/nn.py	8	4	3
def backward() in cm/nn.py	15	4	2
def setup_dist() in cm/dist_util.py	16	4	0
def __init__() in cm/unet.py	14	4	5
def _forward() in cm/unet.py	21	4	3
def forward() in cm/unet.py	19	4	4
def create_named_schedule_sampler() in cm/resample.py	9	4	2
def center_crop_arr() in cm/image_datasets.py	13	4	2
def random_crop_arr() in cm/image_datasets.py	16	4	4
def add_dict_to_argparser() in cm/script_util.py	8	4	2
def str2bool() in cm/script_util.py	9	4	1
def get_generator() in cm/random_util.py	9	4	3