huggingface / open-muse 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 435 units with 6,050 lines of code in units (51.8% of code).

1 very complex units (543 lines of code)
2 complex units (346 lines of code)
13 medium complex units (868 lines of code)
24 simple units (733 lines of code)
395 very simple units (3,560 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 training/train_muse.py	543	92	0
def main() in training/train_maskgit_imagenet.py	270	46	0
def __call__() in training/data.py	76	46	2
def main() in scripts/pre_encode.py	189	24	0
def rename_flax_dict() in scripts/convert_maskgit_vqgan.py	99	22	1
def main() in benchmark/muse_perf.py	170	19	0
def from_pretrained() in muse/modeling_utils.py	124	19	4
def mask_or_random_replace_tokens() in training/train_muse.py	57	16	5
def load_flax_weights_in_pytorch_model() in scripts/convert_maskgit_vqgan.py	56	15	2
def group_by_keys_nothrow() in training/data.py	18	14	5
def load_state_dict() in muse/training_utils.py	31	13	2
def register_to_config() in muse/modeling_utils.py	30	13	1
def load_state_dict() in muse/modeling_ema.py	31	13	2
def num_parameters() in muse/modeling_utils.py	13	12	3
def flatten_omega_conf() in training/train_muse.py	25	11	2
def flatten_omega_conf() in training/train_maskgit_imagenet.py	25	11	2
def __init__() in muse/modeling_transformer_v2.py	51	10	2
def save_checkpoint() in training/train_muse.py	30	10	4
def _init_weights() in muse/modeling_transformer_v2.py	12	9	2
def forward() in muse/modeling_transformer.py	20	9	6
def _init_weights() in muse/modeling_transformer.py	12	9	2
def offline_ema() in scripts/compute_offline_ema.py	29	9	1
def register_to_config() in muse/modeling_utils.py	17	8	2
def forward() in muse/modeling_transformer.py	25	8	4
def sdxl_synthetic_dataset_map() in training/data.py	21	8	1
def main() in scripts/gen_sdxl_synthetic_dataset.py	87	8	0
def generate_and_log() in scripts/log_generations_wandb.py	38	8	1
def forward() in muse/modeling_transformer.py	21	7	6
def generate_and_save_images_coco() in scripts/calculate_fid.py	75	7	1
def chart() in benchmark/muse_chart.py	65	6	4
def config_from_legacy_kwargs() in muse/modeling_transformer_v2.py	16	6	1
def forward() in muse/modeling_transformer_v2.py	28	6	4
def forward() in muse/modeling_transformer.py	19	6	4
def forward() in muse/modeling_transformer.py	26	6	5
def step() in training/optimizer.py	20	6	2
def generate_images() in training/train_maskgit_imagenet.py	27	6	4
def get_aesthetic_score() in training/data.py	13	6	1
def distribute_shards() in scripts/calculate_fid.py	18	6	3
def generate_and_log() in scripts/log_inpainting_images.py	45	6	1
def distribute_shards() in scripts/pre_encode.py	18	6	3
def forward() in muse/modeling_transformer_v2.py	13	5	4
def forward() in muse/modeling_transformer_v2.py	13	5	4
def unfused_rms_norm() in muse/modeling_transformer_v2.py	14	5	4
def token_probability_distributions_per_percent_masked_bucket() in muse/training_utils.py	19	5	3
def get_mask_chedule() in muse/sampling.py	11	5	2
def load_state_dict() in muse/modeling_utils.py	24	5	2
def to_json_string() in muse/modeling_utils.py	12	5	1
def forward() in muse/modeling_movq.py	21	5	3
def __init__() in muse/modeling_movq.py	22	5	5
def forward() in muse/modeling_movq.py	21	5	3