openai / guided-diffusion - 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 275 units with 2,719 lines of code in units (67.0% of code).

0 very complex units (0 lines of code)
0 complex units (0 lines of code)
5 medium complex units (347 lines of code)
16 simple units (348 lines of code)
254 very simple units (2,024 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/classifier_train.py	123	21	0
def main() in scripts/image_sample.py	62	14	0
def main() in scripts/classifier_sample.py	76	12	0
def training_losses() in guided_diffusion/gaussian_diffusion.py	53	12	6
def space_timesteps() in guided_diffusion/respace.py	33	11	2
def run_loop() in guided_diffusion/train_util.py	16	10	1
def _update_shapes() in evaluations/evaluator.py	15	9	1
def update_with_local_losses() in guided_diffusion/resample.py	20	9	3
def main() in scripts/super_res_sample.py	45	8	0
def writekvs() in guided_diffusion/logger.py	25	8	2
def configure() in guided_diffusion/logger.py	24	8	4
def load_data_for_worker() in scripts/super_res_sample.py	24	7	3
def _read_bytes() in evaluations/evaluator.py	15	7	3
def forward_backward() in guided_diffusion/train_util.py	31	7	3
def dumpkvs() in guided_diffusion/logger.py	20	7	1
def mpi_weighted_mean() in guided_diffusion/logger.py	22	7	2
def run_bpd_evaluation() in scripts/image_nll.py	28	6	5
def split_microbatches() in scripts/classifier_train.py	7	6	2
def normal_kl() in guided_diffusion/losses.py	18	6	4
def writekvs() in guided_diffusion/logger.py	24	6	2
def make_output_format() in guided_diffusion/logger.py	14	6	3
def manifold_radii() in evaluations/evaluator.py	25	5	2
def open_npz_array() in evaluations/evaluator.py	15	5	2
def get_param_groups_and_shapes() in guided_diffusion/fp16_util.py	11	5	1
def save() in guided_diffusion/train_util.py	21	5	1
def load_state_dict() in guided_diffusion/dist_util.py	17	5	2
def _list_image_files_recursively() in guided_diffusion/image_datasets.py	10	5	1
def __getitem__() in guided_diffusion/image_datasets.py	17	5	2
def frechet_distance() in evaluations/evaluator.py	30	4	3
def evaluate() in evaluations/evaluator.py	28	4	4
def master_params_to_model_params() in guided_diffusion/fp16_util.py	6	4	2
def state_dict_to_master_params() in guided_diffusion/fp16_util.py	10	4	3
def _load_and_sync_parameters() in guided_diffusion/train_util.py	12	4	1
def _load_ema_parameters() in guided_diffusion/train_util.py	13	4	2
def conv_nd() in guided_diffusion/nn.py	8	4	3
def avg_pool_nd() in guided_diffusion/nn.py	8	4	3
def backward() in guided_diffusion/nn.py	15	4	2
def setup_dist() in guided_diffusion/dist_util.py	16	4	0
def get_named_beta_schedule() in guided_diffusion/gaussian_diffusion.py	15	4	2
def __init__() in guided_diffusion/unet.py	14	4	5
def _forward() in guided_diffusion/unet.py	21	4	3
def forward() in guided_diffusion/unet.py	19	4	4
def forward() in guided_diffusion/unet.py	16	4	3
def center_crop_arr() in guided_diffusion/image_datasets.py	13	4	2
def random_crop_arr() in guided_diffusion/image_datasets.py	16	4	4
def add_dict_to_argparser() in guided_diffusion/script_util.py	8	4	2
def str2bool() in guided_diffusion/script_util.py	9	4	1
def compute_top_k() in scripts/classifier_train.py	6	3	4
def dump_images() in datasets/lsun_bedroom.py	5	3	3
def compute_inception_score() in evaluations/evaluator.py	13	3	3