alibaba / imood - Unit Size

Intro

Unit size measurements show the distribution of size of units of code (methods, functions...).
Units are classified in four categories based on their size (lines of code): 1-20 (small units), 20-50 (medium size units), 51-100 (long units), 101+ (very long units).
You should aim at keeping units small (< 20 lines). Long units may become "bloaters", code that have increased to such gargantuan proportions that they are hard to work with.

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Unit Size Overall

There are 164 units with 1,962 lines of code in units (77.4% of code).

2 very long units (274 lines of code)
4 long units (274 lines of code)
15 medium size units (477 lines of code)
29 small units (423 lines of code)
114 very small units (514 lines of code)

Legend:

101+

51-100

21-50

11-20

1-10

Unit Size per Extension

101+

51-100

21-50

11-20

1-10

Unit Size per Logical Component

primary logical decomposition

101+

51-100

21-50

11-20

1-10

Alternative Visuals

3D view of all units

Longest Units

Top 50 longest units

Unit	# lines	McCabe index	# params
def train() in train.py	158	32	3
def build_dataset() in utils/common.py	116	25	3
def __init__() in datasets/ClassPrioritySampler.py	86	19	16
def __init__() in datasets/MixedPrioritizedSampler.py	70	15	14
def calc_loss() in models/base.py	61	9	7
def calc_aux_loss() in models/base.py	57	9	4
def my_cl_loss_fn3() in utils/loss_fn.py	49	8	8
def build_plain_train_loader() in utils/common.py	47	7	1
def get_args_parser() in train.py	47	2	0
def get_ood_scores() in models/base.py	45	19	4
def shot_acc() in utils/ltr_metrics.py	35	11	4
def __getitem__() in models/feat_pool.py	35	7	2
def parse_logits() in models/base.py	31	11	5
def create_save_path() in train.py	28	8	2
def build_model() in utils/common.py	26	12	6
def __init__() in models/feat_pool.py	24	3	6
def save_curve() in utils/utils.py	23	2	9
def __init__() in models/resnet.py	22	3	8
def generate_outliers() in models/feat_pool.py	22	2	10
def download_imagenet_whole() in datasets/ImageNet_LT/download.py	22	4	1
def shot_ood() in utils/ltr_metrics.py	21	7	2
def reset_weights() in datasets/ClassPrioritySampler.py	20	11	2
def calc_maha_score() in models/feat_pool.py	19	3	3
def reset_weights() in datasets/MixedPrioritizedSampler.py	19	10	2
def parse_ada_ood_logits() in models/base.py	18	13	4
def forward() in models/base.py	17	8	4
def KNN_dis_search_distance() in models/feat_pool.py	17	2	6
def get_leaf() in datasets/MixedPrioritizedSampler.py	17	4	2
def get_leaf() in datasets/ClassPrioritySampler.py	17	4	2
def get_balanced_weights() in datasets/MixedPrioritizedSampler.py	16	5	2
def update_weights() in datasets/ClassPrioritySampler.py	16	6	4
def get_img_num_per_cls() in datasets/ImbalanceCIFAR.py	16	6	4
def calc_gradnorm_per_sample() in models/base.py	15	2	4
def update_delta() in datasets/ClassPrioritySampler.py	15	5	3
def gen_imbalanced_data() in datasets/ImbalanceCIFAR.py	15	2	2
def build_prior() in utils/common.py	14	5	6
def compute_dist() in utils/loss_fn.py	14	2	3
def get_balanced_weights() in datasets/ClassPrioritySampler.py	14	4	2
def shot_ood2() in utils/ltr_metrics.py	13	4	2
def __init__() in datasets/ClassPrioritySampler.py	13	3	6
def __init__() in datasets/ImbalanceImageNet.py	13	5	5
def class_aware_sample_generator() in datasets/ClassAwareSampler.py	13	4	4
def __init__() in models/resnet.py	12	3	6
def KNN_dis_search_decrease() in models/feat_pool.py	12	1	4
def tinyimages300k_dataloaders() in datasets/tinyimages_300k.py	12	1	4
def __getitem__() in datasets/SCOODBenchmarkDataset.py	12	2	2
def accuracy() in utils/utils.py	11	2	3
def __init__() in datasets/MixedPrioritizedSampler.py	11	3	5
def update_whole() in datasets/ClassPrioritySampler.py	11	7	2
def update() in datasets/ClassPrioritySampler.py	11	3	3