openai / consistency_models_cifar10

Intro

For duplication, we look at places in code where there are 6 or more lines of code that are exactly the same.
Before duplication is calculated, the code is cleaned to remove empty lines, comments, and frequently duplicated constructs such as imports.
You should aim at having as little as possible (<5%) of duplicated code as high-level of duplication can lead to maintenance difficulties, poor factoring, and logical contradictions.

Learn more...

Duplication Overall

27% duplication:

5,332 cleaned lines of cleaned code (without empty lines, comments, and frequently duplicated constructs such as imports)
1,457 duplicated lines

108 duplicates

dependency graphs: 2D graph | 3D graph | 3D graph (with duplicates)...

Duplication per Extension

Duplication per Component (primary)

Longest Duplicates

The list of 50 longest duplicates.

See data for all 108 duplicates...

Size	#	Folders	Files	Lines	Code
44	x 2	configs configs	cifar10_ve_ct_adaptive.py cifar10_ve_ct_ema.py	33:86 (65%) 31:83 (67%)	view
35	x 2	jcm jcm	losses.py losses.py	751:786 (4%) 848:883 (4%)	view
31	x 2	configs configs	cifar10_ve_continuous.py cifar10_ve_ct_ema.py	29:65 (51%) 34:70 (47%)	view
31	x 2	configs configs	cifar10_ve_cd.py cifar10_ve_ct_adaptive.py	40:74 (49%) 44:79 (46%)	view
31	x 2	configs configs	cifar10_ve_cd.py cifar10_ve_ct_ema.py	40:74 (49%) 42:76 (47%)	view
31	x 2	configs configs	cifar10_ve_continuous.py cifar10_ve_ct_adaptive.py	29:65 (51%) 36:72 (46%)	view
30	x 2	jcm jcm	losses.py losses.py	621:651 (3%) 849:879 (3%)	view
30	x 2	jcm jcm	losses.py losses.py	621:651 (3%) 752:782 (3%)	view
27	x 2	jcm jcm	evaluate.py train.py	61:87 (9%) 60:87 (11%)	view
27	x 2	configs configs	cifar10_ve_cd.py cifar10_ve_continuous.py	40:68 (42%) 37:65 (45%)	view
27	x 2	jcm/models jcm/models	ncsnv2.py ncsnv2.py	203:234 (9%) 275:306 (9%)	view
25	x 2	configs configs	cifar10_ve_ct_adaptive.py cifar10_ve_progressive_distillation.py	51:78 (37%) 47:73 (38%)	view
25	x 2	configs configs	cifar10_ve_ct_ema.py cifar10_ve_progressive_distillation.py	49:75 (38%) 47:73 (38%)	view
25	x 2	configs configs	cifar10_ve_cd.py cifar10_ve_progressive_distillation.py	47:73 (39%) 47:73 (38%)	view
22	x 2	configs configs	cifar10_ve_continuous.py cifar10_ve_progressive_distillation.py	44:65 (36%) 47:68 (33%)	view
21	x 2	jcm/models jcm/models	ncsnv2.py ncsnv2.py	55:80 (7%) 275:300 (7%)	view
21	x 2	jcm/models jcm/models	ncsnv2.py ncsnv2.py	55:80 (7%) 203:228 (7%)	view
20	x 2	jcm jcm	sampling.py sampling.py	558:579 (3%) 605:626 (3%)	view
20	x 2	configs configs	cifar10_k_ve.py cifar10_k_vp.py	5:31 (35%) 5:32 (35%)	view
20	x 2	jcm jcm	sampling.py sampling.py	306:326 (3%) 357:377 (3%)	view
20	x 2	jcm jcm	train.py train.py	99:120 (8%) 263:284 (8%)	view
18	x 2	jcm jcm	sde_lib.py sde_lib.py	257:277 (5%) 360:380 (5%)	view
16	x 2	configs configs	cifar10_k_ve.py cifar10_ve_continuous.py	35:50 (28%) 44:59 (26%)	view
16	x 2	configs configs	cifar10_k_ve.py cifar10_ve_ct_adaptive.py	35:50 (28%) 51:66 (23%)	view
16	x 2	configs configs	cifar10_k_ve.py cifar10_ve_progressive_distillation.py	35:50 (28%) 47:62 (24%)	view
16	x 2	configs configs	cifar10_k_ve.py cifar10_ve_ct_ema.py	35:50 (28%) 49:64 (24%)	view
16	x 2	configs configs	cifar10_k_ve.py cifar10_ve_cd.py	35:50 (28%) 47:62 (25%)	view
15	x 2	jcm/models jcm/models	ncsnv2.py ncsnv2.py	238:253 (5%) 316:331 (5%)	view
14	x 2	jcm/models jcm/models	layers.py layers.py	443:456 (2%) 489:502 (2%)	view
13	x 2	jcm jcm	sde_lib.py sde_lib.py	216:231 (3%) 329:344 (3%)	view
13	x 2	configs configs	cifar10_k_ve.py cifar10_k_vp.py	55:70 (23%) 56:71 (23%)	view
13	x 2	jcm jcm	metrics.py metrics.py	56:72 (7%) 92:108 (7%)	view
12	x 2	jcm jcm	sampling.py sampling.py	586:598 (2%) 627:639 (2%)	view
11	x 2	jcm/models jcm/models	layers.py layers.py	382:392 (2%) 406:416 (2%)	view
11	x 2	jcm/models jcm/models	layerspp.py wideresnet_noise_conditional.py	33:46 (6%) 213:226 (7%)	view
10	x 2	jcm jcm	losses.py losses.py	700:711 (1%) 813:824 (1%)	view
10	x 2	jcm/models jcm/models	ncsnv2.py ncsnv2.py	257:268 (3%) 338:349 (3%)	view
10	x 2	jcm jcm	losses.py losses.py	350:359 (1%) 453:462 (1%)	view
9	x 2	jcm jcm	sde_lib.py sde_lib.py	193:202 (2%) 306:315 (2%)	view
9	x 2	configs configs	cifar10_ve_ct_adaptive.py cifar10_ve_ct_ema.py	16:24 (13%) 16:24 (13%)	view
9	x 2	jcm jcm	losses.py losses.py	714:722 (1%) 731:740 (1%)	view
9	x 2	jcm/models jcm/models	layerspp.py layerspp.py	92:101 (5%) 123:132 (5%)	view
9	x 2	jcm/models jcm/models	layers.py layers.py	227:235 (1%) 255:263 (1%)	view
8	x 2	jcm/models jcm/models	layers.py layerspp.py	582:590 (1%) 75:83 (4%)	view
8	x 2	configs configs	cifar10_ve_cd.py cifar10_ve_ct_adaptive.py	25:36 (12%) 30:41 (11%)	view
8	x 2	jcm jcm	losses.py losses.py	731:738 (<1%) 805:812 (<1%)	view
8	x 2	configs configs	cifar10_k_vp.py cifar10_ve_ct_adaptive.py	33:40 (14%) 51:58 (11%)	view
8	x 2	jcm jcm	losses.py losses.py	714:721 (<1%) 805:812 (<1%)	view
8	x 2	configs configs	cifar10_k_ve.py cifar10_k_vp.py	35:42 (14%) 33:40 (14%)	view
8	x 2	jcm/models jcm/models	utils.py utils.py	178:186 (3%) 210:218 (3%)	view