Precision: [tensor(0.0520, device='cuda:0'), tensor(0.0455, device='cuda:0'), tensor(0.0640, device='cuda:0'), tensor(0.0393, device='cuda:0'), tensor(0.0507, device='cuda:0'), tensor(0.0459, device='cuda:0'), tensor(0.0777, device='cuda:0'), tensor(0.0417, device='cuda:0'), tensor(0.0472, device='cuda:0'), tensor(0.0567, device='cuda:0')]

Output distance: [tensor(21.7134, device='cuda:0'), tensor(21.7524, device='cuda:0'), tensor(21.6415, device='cuda:0'), tensor(21.7896, device='cuda:0'), tensor(21.7210, device='cuda:0'), tensor(21.7500, device='cuda:0'), tensor(21.5589, device='cuda:0'), tensor(21.7754, device='cuda:0'), tensor(21.7421, device='cuda:0'), tensor(21.6853, device='cuda:0')]

Prediction loss: [tensor(97.1209, device='cuda:0'), tensor(98.4889, device='cuda:0'), tensor(96.9450, device='cuda:0'), tensor(99.2005, device='cuda:0'), tensor(95.6618, device='cuda:0'), tensor(96.3669, device='cuda:0'), tensor(98.9985, device='cuda:0'), tensor(98.2078, device='cuda:0'), tensor(100.3109, device='cuda:0'), tensor(97.2257, device='cuda:0')]

Others: [{'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}]

Compressed training loss: [tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=596327), datetime.timedelta(seconds=1, microseconds=599981), datetime.timedelta(seconds=1, microseconds=603139), datetime.timedelta(seconds=1, microseconds=596077), datetime.timedelta(seconds=1, microseconds=598936), datetime.timedelta(seconds=1, microseconds=602066), datetime.timedelta(seconds=1, microseconds=589510), datetime.timedelta(seconds=1, microseconds=593998), datetime.timedelta(seconds=1, microseconds=607171), datetime.timedelta(seconds=1, microseconds=590411)]

Phi time: [datetime.timedelta(seconds=170, microseconds=31487), datetime.timedelta(seconds=170, microseconds=387273), datetime.timedelta(seconds=170, microseconds=329205), datetime.timedelta(seconds=170, microseconds=756447), datetime.timedelta(seconds=170, microseconds=525687), datetime.timedelta(seconds=170, microseconds=472662), datetime.timedelta(seconds=170, microseconds=655778), datetime.timedelta(seconds=170, microseconds=119536), datetime.timedelta(seconds=170, microseconds=258869), datetime.timedelta(seconds=170, microseconds=815704)]

