pytorch / benchmark

Conditional Complexity

The distribution of complexity of units (measured with McCabe index).

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 2,524 units with 29,612 lines of code in units (53.4% of code).

1 very complex units (308 lines of code)
13 complex units (1,391 lines of code)
116 medium complex units (4,798 lines of code)
210 simple units (4,978 lines of code)
2,184 very simple units (18,137 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 prepare_training_loop() in torchbenchmark/models/yolov3/yolo_train.py	308	107	1
def test() in torchbenchmark/models/yolov3/test.py	153	49	13
def detect() in torchbenchmark/models/yolov3/detect.py	106	45	1
def __init__() in torchbenchmark/models/yolov3/yolo_utils/datasets.py	135	44	11
def create_modules() in torchbenchmark/models/yolov3/yolo_models.py	101	41	3
def main_worker() in torchbenchmark/models/moco/main_lincls.py	132	41	3
def train_multi() in torchbenchmark/models/pytorch_stargan/solver.py	121	35	1
def train_one_epoch() in torchbenchmark/util/framework/timm/train.py	54	31	13
def main() in torchbenchmark/models/demucs/demucs/__main__.py	161	31	0
def main_worker() in torchbenchmark/models/moco/main_moco.py	114	31	3
def timm_instantiate_train() in torchbenchmark/util/framework/timm/instantiate.py	120	30	1
def train() in torchbenchmark/models/pytorch_stargan/solver.py	102	30	2
def parse_model_cfg() in torchbenchmark/models/yolov3/yolo_utils/parse_config.py	39	29	1
def sgd_optimizer() in torchbenchmark/models/dlrm/dlrm_s_caffe2.py	53	26	5
def preprocess_dataset() in torchbenchmark/util/framework/transformers/text_classification/dataset.py	56	25	9
def plot_images() in torchbenchmark/models/yolov3/yolo_utils/utils.py	58	25	7
def update_pair_statistics() in torchbenchmark/models/attention_is_all_you_need_pytorch/learn_bpe.py	40	24	4
def TrainInit() in torchbenchmark/models/nvidia_deeprecommender/nvtrain.py	101	22	5
def __init__() in torchbenchmark/models/nvidia_deeprecommender/reco_encoder/data/input_layer.py	38	22	4
def parallel_forward() in torchbenchmark/models/dlrm/dlrm_s_pytorch.py	52	22	4
def prepare_training_loop() in torchbenchmark/models/pytorch_CycleGAN_and_pix2pix/train_cyclegan.py	40	21	1
def train_epoch() in torchbenchmark/models/timm_efficientdet/train.py	37	21	12
def encode() in torchbenchmark/models/attention_is_all_you_need_pytorch/apply_bpe.py	40	21	9
def train() in torchbenchmark/models/tacotron2/waveglow/tacotron2/train.py	75	21	8
def prep() in torchbenchmark/e2e_models/hf_bert/trainer.py	65	21	4
def main() in torchbenchmark/models/Super_SloMo/video_to_slomo.py	86	20	0
def numericalize() in torchbenchmark/util/torchtext_legacy/field.py	36	19	3
def read() in torchbenchmark/models/demucs/demucs/audio.py	53	19	4
def train() in torchbenchmark/models/tacotron2/waveglow/train.py	66	19	13
def train() in torchbenchmark/models/tacotron2/train_tacotron2.py	76	19	8
def forward() in torchbenchmark/models/maml/learner.py	47	18	4
def create_NETFLIX_data_timesplit() in torchbenchmark/models/nvidia_deeprecommender/data_utils/netflix_data_convert.py	38	18	5
def main() in torchbenchmark/models/nvidia_deeprecommender/data_utils/movie_lense_data_converter.py	71	18	1
def __getitem__() in torchbenchmark/models/yolov3/yolo_utils/datasets.py	50	18	2
def forward_once() in torchbenchmark/models/yolov3/yolo_models.py	44	18	4
def __init__() in torchbenchmark/models/tacotron2/waveglow/tacotron2/distributed.py	40	18	2
def __init__() in torchbenchmark/models/tacotron2/distributed.py	40	18	2
def __init__() in torchbenchmark/util/torchtext_legacy/vocab.py	32	17	6
def __init__() in torchbenchmark/models/nvidia_deeprecommender/nvinfer.py	67	17	5
def __init__() in torchbenchmark/models/dcgan/__init__.py	37	17	6
def __getitem__() in torchbenchmark/models/dlrm/dlrm_data_pytorch.py	30	17	2
def apply_gradient_allreduce() in torchbenchmark/models/tacotron2/waveglow/distributed.py	43	17	1
def apply_gradient_allreduce() in torchbenchmark/models/tacotron2/distributed.py	42	17	1
def recognize_beam() in torchbenchmark/models/speech_transformer/speech_transformer/transformer/decoder.py	63	17	4
def prep_dataset() in torchbenchmark/util/framework/transformers/text_classification/dataset.py	20	16	1
def __call__() in torchbenchmark/models/vision_maskrcnn/coco_utils.py	42	16	3
def select_device() in torchbenchmark/models/yolov3/yolo_utils/torch_utils.py	22	16	3
def train() in torchbenchmark/models/LearningToPaint/baseline_modelfree/train.py	57	16	3
def train() in torchbenchmark/models/LearningToPaint/baseline/train.py	57	16	3
def prep() in torchbenchmark/e2e_models/hf_bert/__init__.py	58	16	2