openai / glow - 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 107 units with 1,882 lines of code in units (88.4% of code).

0 very complex units (0 lines of code)
1 complex units (196 lines of code)
4 medium complex units (276 lines of code)
19 simple units (559 lines of code)
83 very simple units (851 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 gradients() in memory_saving_gradients.py	196	36	5
def train() in train.py	60	20	5
def model() in model.py	124	14	5
def revnet2d_step() in model.py	55	12	5
def actnorm_scale() in tfops.py	37	11	9
def get_data() in data_loaders/get_mnist_cifar.py	66	9	8
def get_data() in train.py	42	8	2
def conv2d() in tfops.py	26	8	13
def shuffle_features() in tfops.py	29	8	5
def dump_celebahq() in data_loaders/generate_tfr/generate.py	42	8	5
def abstract_model_xy() in model.py	51	8	8
def format_ops() in memory_saving_gradients.py	8	7	2
def to_raster_old() in graphics.py	24	7	3
def to_raster() in graphics.py	23	7	3
def add_edge_padding() in tfops.py	30	7	2
def dump() in data_loaders/generate_tfr/imagenet_oord.py	50	7	6
def tf_toposort() in memory_saving_gradients.py	14	6	2
def init_visualizations() in train.py	32	6	3
def main() in train.py	17	6	1
def print_act_stats() in tfops.py	14	6	2
def actnorm() in tfops.py	17	6	9
def input_fn() in data_loaders/get_data.py	17	6	9
def adam2() in optim.py	31	6	5
def adam2_old() in optim.py	26	6	6
def my_add_control_inputs() in memory_saving_gradients.py	4	5	2
def infer() in train.py	19	5	4
def actnorm_center() in tfops.py	17	5	3
def conv2d_zeros() in tfops.py	20	5	11
def dump_imagenet() in data_loaders/generate_tfr/generate.py	40	5	5
def __init__() in data_loaders/generate_tfr/generate.py	34	5	7
def close() in data_loaders/generate_tfr/generate.py	10	5	1
def adam() in optim.py	28	5	5
def adamax() in optim.py	28	5	5
def adam() in optim.py	28	5	5
def codec() in model.py	16	5	1
def prior() in model.py	24	5	3
def invertible_1x1_conv() in model.py	73	5	4
def debug_print() in memory_saving_gradients.py	4	4	2
def get_its() in train.py	11	4	1
def linear() in tfops.py	14	4	7
def parse_celeba_image() in data_loaders/generate_tfr/generate.py	27	4	2
def add_image() in data_loaders/generate_tfr/generate.py	19	4	4
def revnet2d() in model.py	11	4	5
def _to_ops() in memory_saving_gradients.py	4	3	1
def separable_conv2d() in tfops.py	21	3	10
def squeeze2d() in tfops.py	15	3	2
def unsqueeze2d() in tfops.py	15	3	2
def flatten_sum() in tfops.py	7	3	1
def parse_tfrecord_tf() in data_loaders/get_data.py	13	3	3
def parse_image() in data_loaders/generate_tfr/generate.py	29	3	1