in detector/train.py [0:0]
def accuracy_sum(logits, labels):
if list(logits.shape) == list(labels.shape) + [2]:
# 2-d outputs
classification = (logits[..., 0] < logits[..., 1]).long().flatten()
else:
classification = (logits > 0).long().flatten()
assert classification.shape == labels.shape
return (classification == labels).float().sum().item()