openai / improved-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 198 units with 1,825 lines of code in units (67.6% of code).

0 very complex units (0 lines of code)
0 complex units (0 lines of code)
3 medium complex units (146 lines of code)
13 simple units (313 lines of code)
182 very simple units (1,366 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/image_sample.py	60	13	0
def training_losses() in improved_diffusion/gaussian_diffusion.py	53	12	6
def space_timesteps() in improved_diffusion/respace.py	33	11	2
def run_loop() in improved_diffusion/train_util.py	16	10	1
def update_with_local_losses() in improved_diffusion/resample.py	20	9	3
def forward_backward() in improved_diffusion/train_util.py	35	8	3
def writekvs() in improved_diffusion/logger.py	25	8	2
def configure() in improved_diffusion/logger.py	24	8	4
def main() in scripts/super_res_sample.py	43	7	0
def load_data_for_worker() in scripts/super_res_sample.py	24	7	3
def dumpkvs() in improved_diffusion/logger.py	20	7	1
def mpi_weighted_mean() in improved_diffusion/logger.py	22	7	2
def run_bpd_evaluation() in scripts/image_nll.py	28	6	5
def normal_kl() in improved_diffusion/losses.py	18	6	4
def writekvs() in improved_diffusion/logger.py	24	6	2
def make_output_format() in improved_diffusion/logger.py	14	6	3
def save() in improved_diffusion/train_util.py	21	5	1
def _list_image_files_recursively() in improved_diffusion/image_datasets.py	10	5	1
def __getitem__() in improved_diffusion/image_datasets.py	22	5	2
def main() in datasets/cifar10.py	17	4	0
def _load_and_sync_parameters() in improved_diffusion/train_util.py	12	4	1
def _load_ema_parameters() in improved_diffusion/train_util.py	13	4	2
def optimize_fp16() in improved_diffusion/train_util.py	14	4	1
def conv_nd() in improved_diffusion/nn.py	8	4	3
def avg_pool_nd() in improved_diffusion/nn.py	8	4	3
def backward() in improved_diffusion/nn.py	15	4	2
def setup_dist() in improved_diffusion/dist_util.py	15	4	0
def get_named_beta_schedule() in improved_diffusion/gaussian_diffusion.py	15	4	2
def forward() in improved_diffusion/unet.py	19	4	4
def get_feature_vectors() in improved_diffusion/unet.py	19	4	4
def add_dict_to_argparser() in improved_diffusion/script_util.py	8	4	2
def str2bool() in improved_diffusion/script_util.py	9	4	1
def dump_images() in datasets/lsun_bedroom.py	5	3	3
def __init__() in improved_diffusion/respace.py	14	3	3
def master_params_to_model_params() in improved_diffusion/fp16_util.py	6	3	2
def zero_grad() in improved_diffusion/fp16_util.py	5	3	1
def _load_optimizer_state() in improved_diffusion/train_util.py	11	3	1
def _anneal_lr() in improved_diffusion/train_util.py	7	3	1
def _master_params_to_state_dict() in improved_diffusion/train_util.py	10	3	2
def _state_dict_to_master_params() in improved_diffusion/train_util.py	6	3	2
def parse_resume_step_from_filename() in improved_diffusion/train_util.py	9	3	1
def find_ema_checkpoint() in improved_diffusion/train_util.py	8	3	3
def log_loss_dict() in improved_diffusion/train_util.py	6	3	3
def process_xstart() in improved_diffusion/gaussian_diffusion.py	6	3	1
def forward() in improved_diffusion/unet.py	7	3	3
def forward() in improved_diffusion/unet.py	11	3	2
def __init__() in improved_diffusion/unet.py	10	3	4
def _forward() in improved_diffusion/unet.py	14	3	3
def create_named_schedule_sampler() in improved_diffusion/resample.py	7	3	2
def update_with_all_losses() in improved_diffusion/resample.py	8	3	3