facebookresearch / augmentation-corruption
File Size

The distribution of size of files (measured in lines of code).

Intro
  • File size measurements show the distribution of size of files.
  • Files are classified in four categories based on their size (lines of code): 1-100 (very small files), 101-200 (small files), 201-500 (medium size files), 501-1000 (long files), 1001+(very long files).
  • It is a good practice to keep files small. Long files may become "bloaters", code that have increased to such gargantuan proportions that they are hard to work with.
Learn more...
File Size Overall
  • There are 90 files with 7,284 lines of code.
    • 0 very long files (0 lines of code)
    • 3 long files (2,027 lines of code)
    • 5 medium size files (1,564 lines of codeclsfd_ftr_w_mp_ins)
    • 13 small files (1,716 lines of code)
    • 69 very small files (1,977 lines of code)
0% | 27% | 21% | 23% | 27%
Legend:
1001+
501-1000
201-500
101-200
1-100


explore: zoomable circles | sunburst | 3D view
File Size per Extension
1001+
501-1000
201-500
101-200
1-100
py0% | 30% | 23% | 25% | 21%
yaml0% | 0% | 0% | 0% | 100%
p0% | 0% | 0% | 0% | 100%
File Size per Logical Decomposition
primary
1001+
501-1000
201-500
101-200
1-100
imagenet_c_bar0% | 74% | 0% | 0% | 25%
experiments/overlap0% | 47% | 0% | 42% | 10%
experiments/overlap/augmentations0% | 19% | 48% | 21% | 10%
experiments0% | 0% | 25% | 33% | 41%
notebook_utils0% | 0% | 0% | 71% | 28%
experiments/tools0% | 0% | 0% | 54% | 45%
experiments/conf/train0% | 0% | 0% | 0% | 100%
experiments/conf0% | 0% | 0% | 0% | 100%
imagenet_c_bar/utils0% | 0% | 0% | 0% | 100%
experiments/conf/ft0% | 0% | 0% | 0% | 100%
experiments/conf/optim0% | 0% | 0% | 0% | 100%
experiments/conf/ft_augmentation0% | 0% | 0% | 0% | 100%
experiments/overlap/utils0% | 0% | 0% | 0% | 100%
experiments/conf/corrupt0% | 0% | 0% | 0% | 100%
experiments/conf/model0% | 0% | 0% | 0% | 100%
experiments/baseline_data0% | 0% | 0% | 0% | 100%
experiments/conf/ft_corrupt0% | 0% | 0% | 0% | 100%
Longest Files (Top 50)
File# lines# units
corrupt.py
in imagenet_c_bar
 891 78
datasets.py
in experiments/overlap
 610 47
imagenetc.py
in experiments/overlap/augmentations
 526 64
pil.py
in experiments/overlap/augmentations
 447 76
additive_noise.py
in experiments/overlap/augmentations
 331 30
distortion.py
in experiments/overlap/augmentations
 295 16
sample_datasets.py
in experiments
 279 8
compositions.py
in experiments/overlap/augmentations
 212 25
models.py
in experiments/overlap
 174 18
obscure.py
in experiments/overlap/augmentations
 169 18
blurs.py
in experiments/overlap/augmentations
 162 18
train_net_jsd.py
in experiments/overlap
 143 5
color.py
in experiments/overlap/augmentations
 133 12
closest_augs.py
in experiments
 122 2
severity_scan_imagenet.py
in experiments
 122 2
severity_scan.py
in experiments
 119 2
aug_finder.py
in experiments/overlap/augmentations/utils
 118 4
train_net.py
in experiments/overlap
 117 3
wideresnet.py
in notebook_utils
 113 7
summarize.py
in experiments/tools
 112 1
wideresnet.py
in experiments/overlap
 112 7
calc_distance_shifts.py
in experiments
 100 4
make_imagenet_c_bar.py
in imagenet_c_bar
 93 4
feature_corrupt_error.py
in experiments
 90 1
train_imagenet_jsd.py
in experiments
 89 2
train_imagenet.py
in experiments
 87 2
extract_features.py
in experiments/overlap
 83 2
get_target_error.py
in experiments/tools
 76 4
make_cifar10_c_bar.py
in imagenet_c_bar
 76 2
base.py
in experiments/overlap/augmentations
 66 7
transform_finder.py
in imagenet_c_bar
 66 4
base.py
in imagenet_c_bar
 66 7
noise.py
in experiments/overlap/augmentations/utils
 56 4
perlin_noise.py
in imagenet_c_bar/utils
 56 4
feature_extractor.py
in experiments/overlap
 48 3
train_cifar10_jsd.py
in experiments
 46 1
patch_gaussian.py
in experiments/overlap/augmentations
 46 5
network_imagenet.yaml
in experiments/conf/ft
 46 -
train_cifar10.py
in experiments
 44 1
training_loop.py
in notebook_utils
 44 1
network.yaml
in experiments/conf/ft
 36 -
converters.py
in experiments/overlap/augmentations/utils
 33 6
converters.py
in imagenet_c_bar/utils
 33 6
logging.py
in experiments/overlap/utils
 23 3
image.py
in experiments/overlap/augmentations/utils
 21 2
closest_augs.yaml
in experiments/conf
 21 -
image.py
in imagenet_c_bar/utils
 21 2
sample_image_indices.py
in experiments/tools
 18 1
cifar10_augmix.yaml
in experiments/conf/train
 18 -
severity_scan.yaml
in experiments/conf
 17 -
Files With Most Units (Top 20)
File# lines# units
corrupt.py
in imagenet_c_bar
 891 78
pil.py
in experiments/overlap/augmentations
 447 76
imagenetc.py
in experiments/overlap/augmentations
 526 64
datasets.py
in experiments/overlap
 610 47
additive_noise.py
in experiments/overlap/augmentations
 331 30
compositions.py
in experiments/overlap/augmentations
 212 25
models.py
in experiments/overlap
 174 18
blurs.py
in experiments/overlap/augmentations
 162 18
obscure.py
in experiments/overlap/augmentations
 169 18
distortion.py
in experiments/overlap/augmentations
 295 16
color.py
in experiments/overlap/augmentations
 133 12
sample_datasets.py
in experiments
 279 8
base.py
in experiments/overlap/augmentations
 66 7
wideresnet.py
in experiments/overlap
 112 7
base.py
in imagenet_c_bar
 66 7
wideresnet.py
in notebook_utils
 113 7
converters.py
in experiments/overlap/augmentations/utils
 33 6
converters.py
in imagenet_c_bar/utils
 33 6
train_net_jsd.py
in experiments/overlap
 143 5
patch_gaussian.py
in experiments/overlap/augmentations
 46 5
Files With Long Lines (Top 14)

There are 14 files with lines longer than 120 characters. In total, there are 47 long lines.

File# lines# units# long lines
corrupt.py
in imagenet_c_bar
 891 78 9
datasets.py
in experiments/overlap
 610 47 7
distortion.py
in experiments/overlap/augmentations
 295 16 7
extract_features.py
in experiments/overlap
 83 2 4
compositions.py
in experiments/overlap/augmentations
 212 25 4
imagenetc.py
in experiments/overlap/augmentations
 526 64 4
additive_noise.py
in experiments/overlap/augmentations
 331 30 3
closest_augs.py
in experiments
 122 2 2
sample_datasets.py
in experiments
 279 8 2
p
mCE_baseline_imagenet.p
in experiments/baseline_data
 9 - 1
train_net_jsd.py
in experiments/overlap
 143 5 1
blurs.py
in experiments/overlap/augmentations
 162 18 1
obscure.py
in experiments/overlap/augmentations
 169 18 1
train_net.py
in experiments/overlap
 117 3 1