Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Update dataset_stats() #3593

Merged
merged 1 commit into from
Jun 12, 2021
Merged

Update dataset_stats() #3593

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
15 changes: 12 additions & 3 deletions utils/datasets.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@

import glob
import hashlib
import json
import logging
import math
import os
Expand Down Expand Up @@ -1105,12 +1106,20 @@ def dataset_stats(path='coco128.yaml', autodownload=False, verbose=False):
continue
x = []
dataset = LoadImagesAndLabels(data[split], augment=False, rect=True) # load dataset
if split == 'train':
cache_path = Path(dataset.label_files[0]).parent.with_suffix('.cache') # *.cache path
for label in tqdm(dataset.labels, total=dataset.n, desc='Statistics'):
x.append(np.bincount(label[:, 0].astype(int), minlength=nc))
x = np.array(x) # shape(128x80)
stats[split] = {'instances': {'total': int(x.sum()), 'per_class': x.sum(0).tolist()},
'images': {'total': dataset.n, 'unlabelled': int(np.all(x == 0, 1).sum()),
'per_class': (x > 0).sum(0).tolist()}}
stats[split] = {'instance_stats': {'total': int(x.sum()), 'per_class': x.sum(0).tolist()},
'image_stats': {'total': dataset.n, 'unlabelled': int(np.all(x == 0, 1).sum()),
'per_class': (x > 0).sum(0).tolist()},
'labels': {str(Path(k).name): v.tolist() for k, v in zip(dataset.img_files, dataset.labels)}}

# Save, print and return
with open(cache_path.with_suffix('.json'), 'w') as f:
json.dump(stats, f) # save stats *.json
if verbose:
print(yaml.dump([stats], sort_keys=False, default_flow_style=False))
# print(json.dumps(stats, indent=2, sort_keys=False))
return stats