18 lines
709 B
Python
18 lines
709 B
Python
|
import torch
|
||
|
|
|
||
|
|
def fscore(dist1, dist2, threshold=0.001):
|
||
|
|
"""
|
||
|
|
Calculates the F-score between two point clouds with the corresponding threshold value.
|
||
|
|
:param dist1: Batch, N-Points
|
||
|
|
:param dist2: Batch, N-Points
|
||
|
|
:param th: float
|
||
|
|
:return: fscore, precision, recall
|
||
|
|
"""
|
||
|
|
# NB : In this depo, dist1 and dist2 are squared pointcloud euclidean distances, so you should adapt the threshold accordingly.
|
||
|
|
precision_1 = torch.mean((dist1 < threshold).float(), dim=1)
|
||
|
|
precision_2 = torch.mean((dist2 < threshold).float(), dim=1)
|
||
|
|
fscore = 2 * precision_1 * precision_2 / (precision_1 + precision_2)
|
||
|
|
fscore[torch.isnan(fscore)] = 0
|
||
|
|
return fscore, precision_1, precision_2
|
||
|
|
|