alibaba / catex - 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 810 units with 9,372 lines of code in units (72.0% of code).

1 very complex units (202 lines of code)
2 complex units (135 lines of code)
16 medium complex units (837 lines of code)
55 simple units (1,663 lines of code)
736 very simple units (6,535 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 test_ood() in trainers/catex.py	202	52	3
def load() in clip/clip.py	58	28	3
def calc_loss() in trainers/catex.py	77	26	7
def build_optimizer() in Dassl.pytorch/dassl/optim/optimizer.py	107	21	3
def _build_transform_train() in Dassl.pytorch/dassl/data/transforms/transforms.py	95	19	4
def parse_function() in Dassl.pytorch/tools/parse_test_res.py	46	15	4
def __init__() in trainers/catex.py	63	14	4
def get_msp_scores() in ood/metrics.py	62	14	4
def init_network_weights() in Dassl.pytorch/dassl/modeling/network/ddaig_fcn.py	29	14	3
def init_network_weights() in Dassl.pytorch/dassl/utils/torchtools.py	26	14	3
def forward_backward() in Dassl.pytorch/dassl/engine/da/dael.py	74	14	3
def reset_cfg() in train.py	25	13	2
def build_lr_scheduler() in Dassl.pytorch/dassl/optim/lr_scheduler.py	51	13	2
def build_model() in clip/model.py	33	12	1
def step() in Dassl.pytorch/dassl/optim/radam.py	67	12	2
def main() in train.py	26	11	1
def reset_cfg() in Dassl.pytorch/tools/train.py	21	11	2
def step() in Dassl.pytorch/dassl/optim/radam.py	55	11	2
def run_epoch() in Dassl.pytorch/dassl/engine/trainer.py	57	11	1
def forward() in trainers/catex.py	70	10	4
def forward() in trainers/catex.py	24	10	7
def cache_feat() in trainers/catex.py	51	10	3
def bpe() in clip/simple_tokenizer.py	37	10	2
def convert_weights() in clip/model.py	17	10	1
def read_and_split_data() in datasets/dtd.py	29	10	5
def fpr_and_fdr_at_recall() in ood/metrics.py	26	10	4
def forward() in Dassl.pytorch/dassl/modeling/backbone/efficientnet/model.py	26	10	3
def load_model() in trainers/catex.py	27	9	3
def step() in Dassl.pytorch/dassl/optim/radam.py	48	9	2
def forward_backward() in Dassl.pytorch/dassl/engine/dg/daeldg.py	43	9	2
def perturb_prompt() in trainers/catex.py	32	8	2
def _download() in clip/clip.py	23	8	2
def initialize_parameters() in clip/model.py	24	8	1
def subsample_classes() in datasets/oxford_pets.py	32	8	2
def gen_ood() in synthesis/feature_sample.py	39	8	6
def _init_params() in Dassl.pytorch/dassl/modeling/backbone/resnet.py	18	8	1
def __iter__() in Dassl.pytorch/dassl/data/samplers.py	23	8	1
def open_specified_layers() in Dassl.pytorch/dassl/utils/torchtools.py	17	8	2
def load_pretrained_weights() in Dassl.pytorch/dassl/utils/torchtools.py	30	8	2
def prompt_ensemble() in trainers/catex.py	49	7	2
def build_model() in trainers/catex.py	20	7	1
def load_data() in ood/datasets.py	17	7	2
def _decode_block_string() in Dassl.pytorch/dassl/modeling/backbone/efficientnet/utils.py	22	7	1
def __init__() in Dassl.pytorch/dassl/modeling/backbone/efficientnet/model.py	62	7	4
def __init__() in Dassl.pytorch/dassl/modeling/backbone/efficientnet/model.py	65	7	3
def forward() in Dassl.pytorch/dassl/modeling/ops/mixstyle.py	27	7	2
def forward() in Dassl.pytorch/dassl/modeling/ops/efdmix.py	24	7	2
def after_epoch() in Dassl.pytorch/dassl/engine/trainer.py	20	7	1
def forward_backward() in Dassl.pytorch/dassl/engine/da/m3sda.py	54	7	3
def __init__() in trainers/catex.py	21	6	2