huggingface / instruction-tuned-sd 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).

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Conditional Complexity Overall

There are 27 units with 1,814 lines of code in units (92.4% of code).

2 very complex units (956 lines of code)
0 complex units (0 lines of code)
0 medium complex units (0 lines of code)
3 simple units (678 lines of code)
22 very simple units (180 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 27 most complex units

Unit	# lines	McCabe index	# params
def main() in train_instruct_pix2pix.py	424	122	0
def main() in finetune_instruct_pix2pix.py	532	121	0
def parse_args() in finetune_instruct_pix2pix.py	345	8	0
def parse_args() in train_instruct_pix2pix.py	293	8	0
def main() in validation/compare_models.py	40	6	1
def load_dataset() in validation/data_utils.py	7	3	2
def load_dataset() in data_preparation/generate_dataset.py	7	3	2
def main() in data_preparation/export_to_hub.py	21	3	1
def resize_crop() in data_preparation/image_utils.py	11	3	1
def get_full_repo_name() in train_instruct_pix2pix.py	8	3	3
def main() in data_preparation/generate_dataset.py	17	2	1
def load_instructions() in data_preparation/export_to_hub.py	5	2	1
def generate_examples() in data_preparation/export_to_hub.py	9	2	2
def parse_args() in validation/compare_models.py	22	1	0
def load_pipeline() in validation/compare_models.py	7	1	1
def get_full_repo_name() in finetune_instruct_pix2pix.py	2	1	0
def convert_to_np() in finetune_instruct_pix2pix.py	3	1	2
def download_image() in finetune_instruct_pix2pix.py	5	1	1
def parse_args() in data_preparation/generate_dataset.py	12	1	0
def parse_args() in data_preparation/export_to_hub.py	6	1	0
def download_image() in data_preparation/image_utils.py	5	1	1
def preprocess_image() in data_preparation/image_utils.py	7	1	1
def postprocess_image() in data_preparation/image_utils.py	6	1	1
def load_model() in data_preparation/model_utils.py	5	1	1
def perform_inference() in data_preparation/model_utils.py	7	1	1
def convert_to_np() in train_instruct_pix2pix.py	3	1	2
def download_image() in train_instruct_pix2pix.py	5	1	1