Precision: [tensor(0.9978, device='cuda:0'), tensor(0.9972, device='cuda:0'), tensor(0.9978, device='cuda:0'), tensor(0.9977, device='cuda:0'), tensor(0.9977, device='cuda:0'), tensor(0.9982, device='cuda:0'), tensor(0.9980, device='cuda:0'), tensor(0.9980, device='cuda:0'), tensor(0.9983, device='cuda:0'), tensor(0.9977, device='cuda:0')]
Output distance: [tensor(30398.1348, device='cuda:0'), tensor(30120.8750, device='cuda:0'), tensor(30091.8848, device='cuda:0'), tensor(30101.6602, device='cuda:0'), tensor(30132.9590, device='cuda:0'), tensor(30062.0371, device='cuda:0'), tensor(30168.0371, device='cuda:0'), tensor(30126.7109, device='cuda:0'), tensor(30052.8262, device='cuda:0'), tensor(30150.0215, device='cuda:0')]
Prediction loss: [tensor(32182.5977, device='cuda:0'), tensor(32053.0020, device='cuda:0'), tensor(31628.5762, device='cuda:0'), tensor(32542.1816, device='cuda:0'), tensor(32575.5625, device='cuda:0'), tensor(32470.0508, device='cuda:0'), tensor(32278.5020, device='cuda:0'), tensor(31070.9648, device='cuda:0'), tensor(31907.8027, device='cuda:0'), tensor(31921.7441, device='cuda:0')]
Others: [{'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]
Compressed training loss: [tensor(40088144., device='cuda:0'), tensor(39532056., device='cuda:0'), tensor(39088024., device='cuda:0'), tensor(40279388., device='cuda:0'), tensor(40129960., device='cuda:0'), tensor(40267556., device='cuda:0'), tensor(39955856., device='cuda:0'), tensor(38454092., device='cuda:0'), tensor(39562200., device='cuda:0'), tensor(39397972., device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=578570), datetime.timedelta(microseconds=577622), datetime.timedelta(microseconds=577573), datetime.timedelta(microseconds=497908), datetime.timedelta(microseconds=618401), datetime.timedelta(microseconds=488005), datetime.timedelta(microseconds=652259), datetime.timedelta(microseconds=555665), datetime.timedelta(microseconds=572595), datetime.timedelta(microseconds=582601)]
Phi time: [datetime.timedelta(microseconds=857927), datetime.timedelta(microseconds=866992), datetime.timedelta(microseconds=857123), datetime.timedelta(microseconds=894527), datetime.timedelta(microseconds=898643), datetime.timedelta(microseconds=860796), datetime.timedelta(microseconds=915707), datetime.timedelta(microseconds=861422), datetime.timedelta(microseconds=857842), datetime.timedelta(microseconds=861520)]
