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.
File Size Overall
- There are 84 files with 5,474 lines of code.
- 0 very long files (0 lines of code)
- 3 long files (1,990 lines of code)
- 6 medium size files (1,558 lines of codeclsfd_ftr_w_mp_ins)
- 9 small files (1,342 lines of code)
- 66 very small files (584 lines of code)
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
File Size per Logical Decomposition
primary
Longest Files (Top 50)
| File | # lines | # units |
|---|---|---|
 utils.py in notebooks |
765 | 32 |
train.py in func |
644 | 16 |
base_video_dataset.py in datasets |
581 | 25 |
epic_kitchens.py in datasets |
372 | 19 |
 env.yaml in root |
312 | - |
launch.py in root |
237 | 13 |
transforms.py in common |
224 | 39 |
breakfast_50salads.py in datasets |
211 | 13 |
video_classification.py in models |
202 | 18 |
log.py in common |
198 | 22 |
base_model.py in models |
194 | 6 |
train_eval_ops.py in func |
181 | 9 |
future_prediction.py in models |
175 | 9 |
utils.py in common |
144 | 14 |
temporal_aggregation.py in models |
121 | 13 |
scheduler.py in common |
112 | 10 |
config.yaml in conf |
112 | - |
simclr_infonce.py in loss_fn |
105 | 8 |
sampler.py in common |
83 | 10 |
data.py in datasets |
41 | 1 |
reader_fns.py in datasets |
36 | 4 |
cluster.py in common |
29 | 5 |
train_net.py in root |
28 | 1 |
multidim_xentropy.py in loss_fn |
26 | 3 |
default.yaml in conf/data |
24 | - |
anticipation_train.yaml in conf/dataset/egtea |
18 | - |
anticipation_val.yaml in conf/dataset/egtea |
18 | - |
anticipation_train+val.yaml in conf/dataset/epic_kitchens100 |
18 | - |
anticipation_train.yaml in conf/dataset/epic_kitchens100 |
17 | - |
anticipation_val.yaml in conf/dataset/epic_kitchens100 |
17 | - |
anticipation_train.yaml in conf/dataset/dundee50salads |
13 | - |
anticipation_val.yaml in conf/dataset/dundee50salads |
13 | - |
anticipation_train_minus_val.yaml in conf/dataset/epic_kitchens |
13 | - |
anticipation_val.yaml in conf/dataset/epic_kitchens |
13 | - |
anticipation_train.yaml in conf/dataset/epic_kitchens |
12 | - |
anticipation_test_s2.yaml in conf/dataset/epic_kitchens |
12 | - |
anticipation_test_s1.yaml in conf/dataset/epic_kitchens |
12 | - |
classifiers.py in models |
11 | 2 |
common.yaml in conf/dataset/dundee50salads |
8 | - |
common.yaml in conf/dataset/epic_kitchens |
8 | - |
common.yaml in conf/dataset/egtea |
8 | - |
common.yaml in conf/dataset/epic_kitchens100 |
8 | - |
pred_future_feat.yaml in conf/train_eval_op |
7 | - |
reduce_lr_on_plateau.yaml in conf/opt/scheduler |
7 | - |
warmup_multi_step.yaml in conf/opt/scheduler |
7 | - |
mse.py in loss_fn |
6 | 1 |
simclr_infonce.yaml in conf/train_eval_op/reg_criterion |
4 | - |
abu_farha.yaml in conf/dataset/dundee50salads/annot_reader_fn |
4 | - |
rulstm.yaml in conf/model/temporal_aggregator |
4 | - |
rulstm.yaml in conf/model/temporal_aggregator_after_future_pred |
4 | - |
Files With Most Units (Top 20)
| File | # lines | # units |
|---|---|---|
transforms.py in common |
224 | 39 |
utils.py in notebooks |
765 | 32 |
base_video_dataset.py in datasets |
581 | 25 |
log.py in common |
198 | 22 |
epic_kitchens.py in datasets |
372 | 19 |
video_classification.py in models |
202 | 18 |
train.py in func |
644 | 16 |
utils.py in common |
144 | 14 |
breakfast_50salads.py in datasets |
211 | 13 |
temporal_aggregation.py in models |
121 | 13 |
launch.py in root |
237 | 13 |
scheduler.py in common |
112 | 10 |
sampler.py in common |
83 | 10 |
future_prediction.py in models |
175 | 9 |
train_eval_ops.py in func |
181 | 9 |
simclr_infonce.py in loss_fn |
105 | 8 |
base_model.py in models |
194 | 6 |
cluster.py in common |
29 | 5 |
reader_fns.py in datasets |
36 | 4 |
multidim_xentropy.py in loss_fn |
26 | 3 |
Files With Long Lines (Top 2)
There are 2 files with lines longer than 120 characters. In total, there are 2 long lines.
| File | # lines | # units | # long lines |
|---|---|---|---|
anticipation_train.yaml in conf/dataset/egtea |
18 | - | 1 |
anticipation_val.yaml in conf/dataset/egtea |
18 | - | 1 |