amazon-research / crossnorm-selfnorm
Duplication

Places in code with 6 or more lines that are exactly the same.

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
  • 51% duplication:
    • 4,680 cleaned lines of cleaned code (without empty lines, comments, and frequently duplicated constructs such as imports)
    • 2,427 duplicated lines
  • 172 duplicates
system51% (2,427 lines)
Duplication per Extension
py51% (2,331 lines)
yaml75% (96 lines)
Duplication per Component (primary)
segmentation/tool97% (728 lines)
segmentation/model56% (621 lines)
ROOT41% (410 lines)
models/imagenet51% (240 lines)
models/cifar34% (209 lines)
models100% (110 lines)
segmentation/config/gtav75% (96 lines)
segmentation/util2% (13 lines)

Duplication Between Components (50+ lines)

G models/imagenet models/imagenet segmentation/model segmentation/model models/imagenet--segmentation/model 427 models models models--segmentation/model 220 models/cifar models/cifar models/cifar--models/imagenet 150 models/cifar--segmentation/model 60

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Show more details on duplication between components...
Longest Duplicates
The list of 20 longest duplicates.
See data for all 172 duplicates...
Size#FoldersFilesLinesCode
133 x 2 segmentation/tool
segmentation/tool
train.py
train_cnsn.py
 149:297 (36%)
155:303 (35%)
 view
126 x 2 segmentation/tool
segmentation/tool
train.py
train_cnsn.py
 298:439 (34%)
314:455 (33%)
 view
110 x 2 models
segmentation/model
cnsn.py
cnsn.py
 8:164 (100%)
8:164 (100%)
 view
71 x 2 segmentation/tool
segmentation/tool
train.py
train_cnsn.py
 63:141 (19%)
63:141 (18%)
 view
51 x 2 segmentation/model
segmentation/model
cnsn_resnet.py
resnet.py
 8:81 (13%)
3:74 (24%)
 view
46 x 2 ROOT
ROOT
cifar.py
imagenet.py
 25:70 (11%)
20:65 (9%)
 view
33 x 2 segmentation/model
segmentation/model
cnsn_resnet.py
resnet.py
 160:213 (8%)
74:123 (15%)
 view
30 x 2 models/imagenet
segmentation/model
resnet_cnsn.py
resnet.py
 326:411 (12%)
267:344 (14%)
 view
28 x 2 segmentation/tool
segmentation/tool
train.py
train_cnsn.py
 24:61 (7%)
24:61 (7%)
 view
21 x 2 models/imagenet
models/imagenet
resnet_cnsn.py
resnet_ibn_cnsn.py
 65:91 (8%)
71:96 (9%)
 view
19 x 2 models/imagenet
segmentation/model
resnet_cnsn.py
resnet.py
 8:34 (8%)
8:34 (9%)
 view
19 x 2 models/imagenet
segmentation/model
resnet_cnsn.py
cnsn_resnet.py
 8:34 (8%)
15:41 (4%)
 view
19 x 2 models/cifar
models/cifar
allconv_cnsn.py
densenet_cnsn.py
 126:145 (16%)
184:203 (11%)
 view
18 x 2 segmentation/config/gtav
segmentation/config/gtav
gtav_fcn50.yaml
gtav_fcn50_cnsn.yaml
 39:57 (30%)
49:67 (26%)
 view
18 x 2 segmentation/model
segmentation/model
psanet.py
pspnet.py
 137:155 (9%)
65:83 (18%)
 view
18 x 2 models/cifar
models/cifar
allconv_cnsn.py
wideresnet_cnsn.py
 135:158 (15%)
187:208 (11%)
 view
18 x 2 models/imagenet
models/imagenet
resnet_cnsn.py
resnet_ibn_cnsn.py
 240:266 (7%)
220:245 (7%)
 view
16 x 2 models/imagenet
segmentation/model
resnet_cnsn.py
resnet.py
 134:152 (6%)
133:151 (7%)
 view
15 x 2 ROOT
ROOT
cifar.py
imagenet.py
 227:246 (3%)
361:381 (3%)
 view
14 x 2 models/imagenet
models/imagenet
resnet_cnsn.py
resnet_ibn_cnsn.py
 99:117 (5%)
102:120 (6%)
 view
Duplicated Units
The list of top 20 duplicated units.
See data for all 23 unit duplicates...
Size#FoldersFilesLinesCode
58 x 2 segmentation/tool
segmentation/tool
train.py
train_cnsn.py
 0:0 
0:0 
 view
28 x 2 segmentation/tool
segmentation/tool
train.py
train_cnsn.py
 0:0 
0:0 
 view
26 x 2 segmentation/model
models
cnsn.py
cnsn.py
 0:0 
0:0 
 view
19 x 2 segmentation/model
models
cnsn.py
cnsn.py
 0:0 
0:0 
 view
16 x 2 segmentation/model
segmentation/model
resnet.py
cnsn_resnet.py
 0:0 
0:0 
 view
15 x 2 segmentation/model
models
cnsn.py
cnsn.py
 0:0 
0:0 
 view
15 x 2 segmentation/model
segmentation/model
resnet.py
cnsn_resnet.py
 0:0 
0:0 
 view
14 x 2 segmentation/model
segmentation/model
resnet.py
cnsn_resnet.py
 0:0 
0:0 
 view
13 x 2 ROOT
ROOT
imagenet.py
cifar.py
 0:0 
0:0 
 view
11 x 2 segmentation/model
segmentation/model
resnet.py
cnsn_resnet.py
 0:0 
0:0 
 view
11 x 2 segmentation/model
models
cnsn.py
cnsn.py
 0:0 
0:0 
 view
9 x 2 segmentation/tool
segmentation/tool
train.py
train_cnsn.py
 0:0 
0:0 
 view
8 x 2 segmentation/tool
segmentation/tool
train.py
train_cnsn.py
 0:0 
0:0 
 view
9 x 2 segmentation/model
models
cnsn.py
cnsn.py
 0:0 
0:0 
 view
8 x 2 segmentation/model
models
cnsn.py
cnsn.py
 0:0 
0:0 
 view
10 x 2 segmentation/model
models/imagenet
resnet.py
resnet_cnsn.py
 0:0 
0:0 
 view
10 x 2 segmentation/model
models/imagenet
resnet.py
resnet_cnsn.py
 0:0 
0:0 
 view
13 x 2 segmentation/model
models/imagenet
resnet.py
resnet_cnsn.py
 0:0 
0:0 
 view
13 x 2 segmentation/model
models/imagenet
resnet.py
resnet_cnsn.py
 0:0 
0:0 
 view
8 x 2 segmentation/model
models/imagenet
resnet.py
resnet_cnsn.py
 0:0 
0:0 
 view