Datasets:
stpete2
/
memo

Dataset card Data Studio Files
xet
 Community
1
text
stringlengths
0
184
mapping_result_strs.append(result_str)
for k in timings:
overall_timings[k].extend(timings[k])
print('\nResults')
for s in mapping_result_strs:
print(s)
run_mast3r_pipeline(samples, data_dir, workdir, is_train, mast3r_model, device)
array_to_str = lambda array: ';'.join([f"{x:.09f}" for x in array])
none_to_str = lambda n: ';'.join(['nan'] * n)
submission_file = '/kaggle/working/submission.csv'
with open(submission_file, 'w') as f:
if is_train:
f.write('dataset,scene,image,rotation_matrix,translation_vector\n')
for dataset, predictions in samples.items():
for prediction in predictions:
cluster_name = 'outliers' if prediction.cluster_index is None else f'cluster{prediction.cluster_index}'
# ✅ `rotation` is a list of lists, flatten it
if prediction.rotation is None:
rotation_str = none_to_str(9)
else:
rotation_flat = prediction.rotation.flatten() # flatten 3x3 list -> 9 elems
rotation_str = array_to_str(rotation_flat)
# ✅ `translation` is a flat list
if prediction.translation is None:
translation_str = none_to_str(3)
else:
translation_str = array_to_str(prediction.translation)
f.write(f'{prediction.dataset},{cluster_name},{prediction.filename},{rotation_str},{translation_str}\n')
else:
f.write('image_id,dataset,scene,image,rotation_matrix,translation_vector\n')
for dataset, predictions in samples.items():
for prediction in predictions:
cluster_name = 'outliers' if prediction.cluster_index is None else f'cluster{prediction.cluster_index}'
if prediction.rotation is None:
rotation_str = none_to_str(9)
else:
rotation_flat = prediction.rotation.flatten()
rotation_str = array_to_str(rotation_flat)
if prediction.translation is None:
translation_str = none_to_str(3)
else:
translation_str = array_to_str(prediction.translation)
f.write(f'{prediction.image_id},{prediction.dataset},{cluster_name},{prediction.filename},{rotation_str},{translation_str}\n')
# Preview the output
!head {submission_file}
# Definitely Compute results if running on the training set.
# Do not do this when submitting a notebook for scoring. All you have to do is save your submission to /kaggle/working/submission.csv.
if is_train:
t = time()
final_score, dataset_scores = metric.score(
gt_csv='/kaggle/input/image-matching-challenge-2025/train_labels.csv',
user_csv=submission_file,
thresholds_csv='/kaggle/input/image-matching-challenge-2025/train_thresholds.csv',
mask_csv=None if is_train else os.path.join(data_dir, 'mask.csv'),
inl_cf=0,
strict_cf=-1,
verbose=True,
)
print(f'Computed metric in: {time() - t:.02f} sec.')