Precision: [tensor(0.1306, device='cuda:0'), tensor(0.1291, device='cuda:0'), tensor(0.1288, device='cuda:0'), tensor(0.1276, device='cuda:0'), tensor(0.1292, device='cuda:0'), tensor(0.1279, device='cuda:0'), tensor(0.1307, device='cuda:0'), tensor(0.1292, device='cuda:0'), tensor(0.1293, device='cuda:0'), tensor(0.1308, device='cuda:0')]
Output distance: [tensor(20684200., device='cuda:0'), tensor(20705454., device='cuda:0'), tensor(20713378., device='cuda:0'), tensor(20738334., device='cuda:0'), tensor(20722134., device='cuda:0'), tensor(20724466., device='cuda:0'), tensor(20691710., device='cuda:0'), tensor(20723022., device='cuda:0'), tensor(20709618., device='cuda:0'), tensor(20681196., device='cuda:0')]
Prediction loss: [tensor(12726257., device='cuda:0'), tensor(12784776., device='cuda:0'), tensor(12770716., device='cuda:0'), tensor(12797430., device='cuda:0'), tensor(12821598., device='cuda:0'), tensor(12793375., device='cuda:0'), tensor(12817585., device='cuda:0'), tensor(12760412., device='cuda:0'), tensor(12817557., device='cuda:0'), tensor(12780668., device='cuda:0')]
Others: [{'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]
Compressed training loss: [tensor(2.5814e+11, device='cuda:0'), tensor(2.6043e+11, device='cuda:0'), tensor(2.5929e+11, device='cuda:0'), tensor(2.6025e+11, device='cuda:0'), tensor(2.6078e+11, device='cuda:0'), tensor(2.5997e+11, device='cuda:0'), tensor(2.6068e+11, device='cuda:0'), tensor(2.5972e+11, device='cuda:0'), tensor(2.6101e+11, device='cuda:0'), tensor(2.6046e+11, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=695991), datetime.timedelta(microseconds=694057), datetime.timedelta(microseconds=681112), datetime.timedelta(microseconds=600451), datetime.timedelta(microseconds=801598), datetime.timedelta(microseconds=687090), datetime.timedelta(microseconds=695105), datetime.timedelta(microseconds=676133), datetime.timedelta(microseconds=597518), datetime.timedelta(microseconds=673194)]
Phi time: [datetime.timedelta(microseconds=887067), datetime.timedelta(microseconds=860569), datetime.timedelta(microseconds=888646), datetime.timedelta(microseconds=858757), datetime.timedelta(microseconds=858432), datetime.timedelta(microseconds=852761), datetime.timedelta(microseconds=879390), datetime.timedelta(microseconds=859145), datetime.timedelta(microseconds=860299), datetime.timedelta(microseconds=858778)]
