openai / consistency_models_cifar10 - 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 241 units with 4,688 lines of code in units (84.5% of code).

0 very complex units (0 lines of code)
3 complex units (708 lines of code)
3 medium complex units (145 lines of code)
17 simple units (886 lines of code)
218 very simple units (2,949 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 evaluate() in jcm/evaluate.py	292	50	3
def train() in jcm/train.py	236	47	2
def __call__() in jcm/models/ncsnpp.py	180	36	4
def loss_fn() in jcm/losses.py	61	15	6
def __call__() in jcm/models/ddpm.py	52	12	4
def __call__() in jcm/models/layerspp.py	32	11	4
def get_score_fn() in jcm/models/utils.py	38	10	6
def save_image() in jcm/utils.py	33	10	6
def loss_fn() in jcm/losses.py	127	10	4
def get_sampling_fn() in jcm/sampling.py	58	9	5
def loss_fn() in jcm/losses.py	73	9	6
def upfirdn_2d() in jcm/models/up_or_down_sampling.py	52	8	10
def get_loss_fn() in jcm/losses.py	136	8	5
def get_ema_scales_fn() in jcm/losses.py	54	8	1
def __call__() in jcm/models/layers.py	33	7	2
def __call__() in jcm/models/layers.py	33	7	3
def get_normalization() in jcm/models/normalization.py	20	7	2
def reverse() in jcm/sde_lib.py	28	7	3
def __call__() in jcm/models/layerspp.py	21	6	4
def __call__() in jcm/models/up_or_down_sampling.py	25	6	2
def shake_shake_train() in jcm/models/wideresnet_noise_conditional.py	14	6	3
def get_optimizer() in jcm/losses.py	58	6	1
def step_fn() in jcm/losses.py	83	6	2
def __getitem__() in jcm/metrics.py	9	5	2
def __call__() in jcm/models/layerspp.py	21	5	2
def __call__() in jcm/models/layerspp.py	22	5	2
def get_act() in jcm/models/layers.py	11	5	1
def __call__() in jcm/models/layers.py	18	5	4
def get_sde() in jcm/sde_lib.py	30	5	1
def flatten_dict() in jcm/utils.py	12	5	1
def collate_fn() in jcm/datasets.py	41	4	1
def get_network() in jcm/models/ncsnv2.py	11	4	1
def register_model() in jcm/models/utils.py	14	4	3
def denoiser_distiller_fn() in jcm/models/utils.py	26	4	3
def upsample_conv_2d() in jcm/models/up_or_down_sampling.py	31	4	6
def __call__() in jcm/models/wideresnet_noise_conditional.py	30	4	4
def __call__() in jcm/models/wideresnet_noise_conditional.py	8	4	4
def __call__() in jcm/models/layers.py	16	4	2
def __call__() in jcm/models/layers.py	17	4	3
def __call__() in jcm/models/layers.py	26	4	2
def __call__() in jcm/models/layers.py	40	4	3
def __call__() in jcm/models/lpips.py	15	4	3
def natural_sort() in jcm/checkpoints.py	10	4	2
def save_checkpoint() in jcm/checkpoints.py	18	4	5
def latest_checkpoint() in jcm/checkpoints.py	9	4	2
def register_predictor() in jcm/sampling.py	14	4	3
def register_corrector() in jcm/sampling.py	14	4	3
def __init__() in jcm/sampling.py	10	4	5
def __init__() in jcm/sampling.py	10	4	5
def ode_sampler() in jcm/sampling.py	27	4	3