Precision: [tensor(0.3895, device='cuda:0'), tensor(0.3892, device='cuda:0'), tensor(0.3865, device='cuda:0'), tensor(0.3885, device='cuda:0'), tensor(0.3907, device='cuda:0'), tensor(0.3887, device='cuda:0'), tensor(0.3909, device='cuda:0'), tensor(0.3839, device='cuda:0'), tensor(0.3849, device='cuda:0'), tensor(0.3868, device='cuda:0')]

Output distance: [tensor(19.6883, device='cuda:0'), tensor(19.6901, device='cuda:0'), tensor(19.7062, device='cuda:0'), tensor(19.6947, device='cuda:0'), tensor(19.6814, device='cuda:0'), tensor(19.6932, device='cuda:0'), tensor(19.6799, device='cuda:0'), tensor(19.7219, device='cuda:0'), tensor(19.7161, device='cuda:0'), tensor(19.7047, device='cuda:0')]

Prediction loss: [tensor(104.6240, device='cuda:0'), tensor(104.9987, device='cuda:0'), tensor(104.3207, device='cuda:0'), tensor(104.0904, device='cuda:0'), tensor(104.1035, device='cuda:0'), tensor(105.4013, device='cuda:0'), tensor(105.3446, device='cuda:0'), tensor(105.1267, device='cuda:0'), tensor(104.3075, device='cuda:0'), tensor(104.2192, device='cuda:0')]

Others: [{'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}]

Compressed training loss: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=6, microseconds=248944), datetime.timedelta(seconds=6, microseconds=241194), datetime.timedelta(seconds=6, microseconds=239591), datetime.timedelta(seconds=6, microseconds=218248), datetime.timedelta(seconds=6, microseconds=250163), datetime.timedelta(seconds=6, microseconds=243612), datetime.timedelta(seconds=6, microseconds=310131), datetime.timedelta(seconds=6, microseconds=256020), datetime.timedelta(seconds=6, microseconds=270903), datetime.timedelta(seconds=6, microseconds=243747)]

Phi time: [datetime.timedelta(seconds=4, microseconds=544314), datetime.timedelta(seconds=4, microseconds=650001), datetime.timedelta(seconds=4, microseconds=663646), datetime.timedelta(seconds=4, microseconds=651524), datetime.timedelta(seconds=4, microseconds=628772), datetime.timedelta(seconds=4, microseconds=628126), datetime.timedelta(seconds=4, microseconds=727696), datetime.timedelta(seconds=4, microseconds=636230), datetime.timedelta(seconds=4, microseconds=632365), datetime.timedelta(seconds=4, microseconds=599899)]

