facebookresearch / AVT
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
  • 2% duplication:
    • 5,264 cleaned lines of cleaned code (without empty lines, comments, and frequently duplicated constructs such as imports)
    • 157 duplicated lines
  • 13 duplicates
system2% (157 lines)
Duplication per Extension
yaml14% (109 lines)
py1% (48 lines)
Duplication per Component (primary)
conf/dataset50% (109 lines)
common2% (22 lines)
func1% (14 lines)
datasets1% (12 lines)
ROOT0% (0 lines)
models0% (0 lines)
loss_fn0% (0 lines)
conf/train_eval_op0% (0 lines)
conf0% (0 lines)
conf/model0% (0 lines)
conf/opt0% (0 lines)
conf/data0% (0 lines)
notebooks0% (0 lines)
Longest Duplicates
The list of 13 longest duplicates.
See data for all 13 duplicates...
Size#FoldersFilesLinesCode
11 x 2 common
common
sampler.py
sampler.py
70:82 (14%)
102:114 (14%)
view
9 x 2 conf/dataset/egtea
conf/dataset/egtea
anticipation_train.yaml
anticipation_val.yaml
12:20 (50%)
12:20 (50%)
view
9 x 2 conf/dataset/epic_kitchens100
conf/dataset/epic_kitchens100
anticipation_train+val.yaml
anticipation_train.yaml
13:21 (50%)
12:20 (52%)
view
8 x 2 conf/dataset/epic_kitchens100
conf/dataset/epic_kitchens100
anticipation_train+val.yaml
anticipation_train.yaml
3:11 (44%)
3:11 (47%)
view
8 x 2 conf/dataset/epic_kitchens
conf/dataset/epic_kitchens
anticipation_test_s1.yaml
anticipation_test_s2.yaml
7:14 (66%)
7:14 (66%)
view
7 x 2 conf/dataset/epic_kitchens
conf/dataset/epic_kitchens
anticipation_train.yaml
anticipation_train_minus_val.yaml
3:9 (58%)
3:17 (53%)
view
7 x 2 conf/dataset/epic_kitchens100
conf/dataset/epic_kitchens100
anticipation_train+val.yaml
anticipation_val.yaml
3:10 (38%)
3:10 (41%)
view
7 x 2 func
func
train_eval_ops.py
train_eval_ops.py
117:127 (4%)
170:176 (4%)
view
7 x 2 conf/dataset/dundee50salads
conf/dataset/dundee50salads
anticipation_train.yaml
anticipation_val.yaml
9:15 (53%)
9:15 (53%)
view
7 x 2 conf/dataset/epic_kitchens100
conf/dataset/epic_kitchens100
anticipation_train.yaml
anticipation_val.yaml
3:10 (41%)
3:10 (41%)
view
6 x 2 conf/dataset/epic_kitchens
conf/dataset/epic_kitchens
anticipation_train.yaml
anticipation_val.yaml
3:8 (50%)
3:8 (46%)
view
6 x 2 conf/dataset/epic_kitchens
conf/dataset/epic_kitchens
anticipation_train_minus_val.yaml
anticipation_val.yaml
3:16 (46%)
3:8 (46%)
view
6 x 2 datasets
datasets
breakfast_50salads.py
epic_kitchens.py
268:273 (3%)
486:491 (1%)
view