Precision: [tensor(0.1342, device='cuda:0'), tensor(0.1358, device='cuda:0'), tensor(0.1247, device='cuda:0'), tensor(0.1565, device='cuda:0'), tensor(0.1284, device='cuda:0'), tensor(0.1359, device='cuda:0'), tensor(0.1273, device='cuda:0'), tensor(0.1275, device='cuda:0'), tensor(0.1227, device='cuda:0'), tensor(0.1374, device='cuda:0')]

Output distance: [tensor(21.2201, device='cuda:0'), tensor(21.2104, device='cuda:0'), tensor(21.2769, device='cuda:0'), tensor(21.0862, device='cuda:0'), tensor(21.2551, device='cuda:0'), tensor(21.2101, device='cuda:0'), tensor(21.2615, device='cuda:0'), tensor(21.2606, device='cuda:0'), tensor(21.2893, device='cuda:0'), tensor(21.2010, device='cuda:0')]

Prediction loss: [tensor(107.3154, device='cuda:0'), tensor(104.8571, device='cuda:0'), tensor(106.3575, device='cuda:0'), tensor(107.8713, device='cuda:0'), tensor(105.4731, device='cuda:0'), tensor(106.5853, device='cuda:0'), tensor(105.7968, device='cuda:0'), tensor(105.0148, device='cuda:0'), tensor(106.1325, device='cuda:0'), tensor(104.8122, 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=116, microseconds=398163), datetime.timedelta(seconds=116, microseconds=174160), datetime.timedelta(seconds=115, microseconds=538592), datetime.timedelta(seconds=115, microseconds=703850), datetime.timedelta(seconds=115, microseconds=560401), datetime.timedelta(seconds=116, microseconds=310510), datetime.timedelta(seconds=116, microseconds=716110), datetime.timedelta(seconds=117, microseconds=624694), datetime.timedelta(seconds=117, microseconds=449296), datetime.timedelta(seconds=116, microseconds=637984)]

Phi time: [datetime.timedelta(seconds=4, microseconds=666684), datetime.timedelta(seconds=4, microseconds=597536), datetime.timedelta(seconds=4, microseconds=510506), datetime.timedelta(seconds=4, microseconds=553390), datetime.timedelta(seconds=4, microseconds=560720), datetime.timedelta(seconds=4, microseconds=685429), datetime.timedelta(seconds=4, microseconds=678646), datetime.timedelta(seconds=4, microseconds=71853), datetime.timedelta(seconds=4, microseconds=40271), datetime.timedelta(seconds=4, microseconds=60549)]

