openai / distribution_augmentation - 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 136 units with 1,883 lines of code in units (63.1% of code).

0 very complex units (0 lines of code)
0 complex units (0 lines of code)
9 medium complex units (425 lines of code)
17 simple units (380 lines of code)
110 very simple units (1,078 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 __init__() in graph.py	46	23	10
def aug_eval() in train.py	66	21	2
def get_blocksparse_obj() in train.py	53	18	3
def stack() in train.py	72	18	5
def mpi_train() in train.py	52	16	0
def iter_data_mpi() in datasets.py	32	13	7
def logger() in utils.py	41	12	1
def get_callback() in train.py	30	12	1
def load_variables() in utils.py	33	11	5
def __init__() in datasets.py	45	9	4
def dense_attention() in train.py	37	9	6
def model() in train.py	32	9	1
def tf_print() in utils.py	19	7	6
def blend() in autoaugment.py	13	7	3
def equalize() in autoaugment.py	20	7	1
def _parse_policy_info() in autoaugment.py	9	7	5
def build_and_apply_nas_policy() in autoaugment.py	20	7	3
def sample() in train.py	18	7	1
def bs_adafactor() in optimizer.py	33	7	9
def parse_args_and_update_hparams() in hyperparams.py	11	6	3
def cifar10() in datasets.py	20	6	3
def get_dense_attn_mask() in train.py	31	6	2
def sparse_attention() in train.py	30	6	6
def get_losses() in train.py	17	6	3
def data_aug() in train.py	10	6	3
def transpose() in train.py	15	6	4
def autocontrast() in autoaugment.py	17	5	1
def eval() in train.py	17	5	2
def bs_adam() in optimizer.py	28	5	9
def get_session() in mpi_utils.py	7	4	3
def initialize_image_embedding() in datasets.py	8	4	1
def __init__() in datasets.py	27	4	3
def solarize_add() in autoaugment.py	4	4	3
def select_and_apply_random_policy() in autoaugment.py	8	4	2
def f32_storage_getter() in train.py	17	4	9
def linear() in train.py	17	4	6
def residual_dropout() in train.py	11	4	4
def sample_model() in train.py	49	4	0
def rotate() in train.py	15	4	4
def reverse() in train.py	15	4	4
def draw() in janky_stuff.py	13	4	2
def prereduce_size() in mpi_utils.py	6	3	0
def tile_images() in datasets.py	15	3	4
def __init__() in datasets.py	28	3	3
def flatten() in datasets.py	2	3	1
def shape_list() in utils.py	4	3	1
def wait_until_synced() in utils.py	6	3	2
def backup_files() in utils.py	8	3	3
def log_gradient_values() in utils.py	10	3	4
def save_params() in utils.py	5	3	2