alibaba / catex - Duplication

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.

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Duplication Overall

21% duplication:

12,266 cleaned lines of cleaned code (without empty lines, comments, and frequently duplicated constructs such as imports)
2,666 duplicated lines

269 duplicates

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

Duplication per Extension

Duplication per Component (primary)

Duplication Between Components (50+ lines)

Download: SVG DOT (open online Graphviz editor)

From Component --> To Component	Duplicated Lines	File Pairs	Details
Dassl.pytorch (1%) --> ROOT (66%)	184	1 file pair	details...
Dassl.pytorch (<1%) --> trainers (3%)	65	5 file pairs	details...

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Longest Duplicates

The list of 50 longest duplicates.

See data for all 269 duplicates...

Size	#	Folders	Files	Lines	Code
29	x 2	Dassl.pytorch/dassl/optim Dassl.pytorch/dassl/optim	radam.py radam.py	50:87 (11%) 165:202 (11%)	view
27	x 2	datasets datasets	sun397.py ucf101.py	35:67 (45%) 38:70 (45%)	view
27	x 2	Dassl.pytorch/dassl/engine/da Dassl.pytorch/dassl/engine/dg	dael.py daeldg.py	58:87 (17%) 57:86 (22%)	view
26	x 2	Dassl.pytorch/dassl/engine Dassl.pytorch/dassl/engine	trainer.py trainer.py	540:569 (5%) 597:626 (5%)	view
25	x 2	Dassl.pytorch/dassl/data/datasets/da Dassl.pytorch/dassl/data/datasets/da	office31.py office_home.py	27:58 (75%) 27:58 (75%)	view
24	x 2	configs/trainers/CATEX configs/trainers/CATEX	vit_b16_ep50.yaml vit_l14_ep50.yaml	1:28 (96%) 1:28 (96%)	view
24	x 2	configs/trainers/CATEX configs/trainers/CATEX	rn50_ep50.yaml vit_b16_ep50.yaml	1:28 (96%) 1:28 (96%)	view
24	x 2	configs/trainers/CATEX configs/trainers/CATEX	rn50_ep50.yaml vit_l14_ep50.yaml	1:28 (96%) 1:28 (96%)	view
23	x 2	Dassl.pytorch/dassl/optim Dassl.pytorch/dassl/optim	radam.py radam.py	20:43 (8%) 135:158 (8%)	view
21	x 2	Dassl.pytorch/dassl/modeling/ops Dassl.pytorch/dassl/modeling/ops	efdmix.py mixstyle.py	60:92 (29%) 60:92 (28%)	view
21	x 2	Dassl.pytorch/dassl/data/datasets/da Dassl.pytorch/dassl/data/datasets/da	domainnet.py mini_domainnet.py	46:69 (53%) 35:58 (58%)	view
21	x 2	datasets datasets	cifar_.py imagenet.py	37:61 (32%) 38:62 (17%)	view
20	x 2	datasets datasets	dtd.py sun397.py	28:51 (31%) 36:59 (33%)	view
20	x 2	datasets datasets	food101.py ucf101.py	28:51 (58%) 39:62 (33%)	view
20	x 2	datasets datasets	oxford_flowers.py sun397.py	32:55 (30%) 36:59 (33%)	view
20	x 2	datasets datasets	food101.py sun397.py	28:51 (58%) 36:59 (33%)	view
20	x 2	datasets datasets	dtd.py ucf101.py	28:51 (31%) 39:62 (33%)	view
20	x 2	datasets datasets	caltech101.py sun397.py	36:59 (48%) 36:59 (33%)	view
20	x 2	datasets datasets	caltech101.py ucf101.py	36:59 (48%) 39:62 (33%)	view
20	x 2	datasets datasets	oxford_flowers.py ucf101.py	32:55 (30%) 39:62 (33%)	view
20	x 2	datasets datasets	dtd.py food101.py	28:51 (31%) 28:51 (58%)	view
20	x 2	datasets datasets	dtd.py oxford_flowers.py	28:51 (31%) 32:55 (30%)	view
20	x 2	datasets datasets	eurosat.py sun397.py	41:64 (37%) 36:59 (33%)	view
20	x 2	datasets datasets	caltech101.py food101.py	36:59 (48%) 28:51 (58%)	view
20	x 2	datasets datasets	eurosat.py food101.py	41:64 (37%) 28:51 (58%)	view
20	x 2	datasets datasets	caltech101.py dtd.py	36:59 (48%) 28:51 (31%)	view
20	x 2	datasets datasets	dtd.py eurosat.py	28:51 (31%) 41:64 (37%)	view
20	x 2	datasets datasets	caltech101.py eurosat.py	36:59 (48%) 41:64 (37%)	view
20	x 2	datasets datasets	caltech101.py oxford_flowers.py	36:59 (48%) 32:55 (30%)	view
20	x 2	datasets datasets	eurosat.py oxford_flowers.py	41:64 (37%) 32:55 (30%)	view
20	x 2	datasets datasets	eurosat.py ucf101.py	41:64 (37%) 39:62 (33%)	view
20	x 2	datasets datasets	food101.py oxford_flowers.py	28:51 (58%) 32:55 (30%)	view
19	x 2	datasets datasets	eurosat.py fgvc_aircraft.py	43:64 (35%) 33:54 (37%)	view
19	x 2	datasets datasets	fgvc_aircraft.py sun397.py	33:54 (37%) 38:59 (32%)	view
19	x 2	datasets datasets	food101.py stanford_cars.py	30:51 (55%) 34:55 (34%)	view
19	x 2	datasets datasets	stanford_cars.py sun397.py	34:55 (34%) 38:59 (32%)	view
19	x 2	datasets datasets	fgvc_aircraft.py ucf101.py	33:54 (37%) 41:62 (31%)	view
19	x 2	datasets datasets	fgvc_aircraft.py stanford_cars.py	33:54 (37%) 34:55 (34%)	view
19	x 2	datasets datasets	oxford_flowers.py stanford_cars.py	34:55 (29%) 34:55 (34%)	view
19	x 2	datasets datasets	dtd.py stanford_cars.py	30:51 (29%) 34:55 (34%)	view
19	x 2	datasets datasets	fgvc_aircraft.py oxford_flowers.py	33:54 (37%) 34:55 (29%)	view
19	x 2	datasets datasets	dtd.py fgvc_aircraft.py	30:51 (29%) 33:54 (37%)	view
19	x 2	datasets datasets	stanford_cars.py ucf101.py	34:55 (34%) 41:62 (31%)	view
19	x 2	datasets datasets	eurosat.py stanford_cars.py	43:64 (35%) 34:55 (34%)	view
19	x 2	datasets datasets	caltech101.py stanford_cars.py	38:59 (46%) 34:55 (34%)	view
19	x 2	datasets datasets	fgvc_aircraft.py food101.py	33:54 (37%) 30:51 (55%)	view
19	x 2	datasets datasets	caltech101.py fgvc_aircraft.py	38:59 (46%) 33:54 (37%)	view
19	x 2	Dassl.pytorch/tools ROOT	train.py train.py	27:55 (13%) 54:82 (13%)	view
18	x 2	Dassl.pytorch/dassl/engine/da Dassl.pytorch/dassl/engine/ssl	source_only.py sup_baseline.py	13:34 (90%) 11:32 (90%)	view
18	x 2	Dassl.pytorch/configs/trainers/dg/ddaig Dassl.pytorch/configs/trainers/dg/ddaig	office_home_dg.yaml pacs.yaml	1:21 (100%) 1:21 (100%)	view

Duplicated Units

The list of top 2 duplicated units.

See data for all 2 unit duplicates...

Size	#	Folders	Files	Lines	Code
19	x 2	Dassl.pytorch/dassl/data/datasets/da Dassl.pytorch/dassl/data/datasets/da	mini_domainnet.py domainnet.py	0:0 0:0	view
10	x 2	Dassl.pytorch/dassl/engine/ssl Dassl.pytorch/dassl/engine/da	sup_baseline.py source_only.py	0:0 0:0	view