Precision: [tensor(0.2127, device='cuda:0'), tensor(0.2152, device='cuda:0'), tensor(0.2155, device='cuda:0'), tensor(0.2142, device='cuda:0'), tensor(0.2112, device='cuda:0'), tensor(0.2117, device='cuda:0'), tensor(0.2145, device='cuda:0'), tensor(0.2153, device='cuda:0'), tensor(0.2162, device='cuda:0'), tensor(0.2172, device='cuda:0')]
Output distance: [tensor(20445902., device='cuda:0'), tensor(20440936., device='cuda:0'), tensor(20426886., device='cuda:0'), tensor(20433560., device='cuda:0'), tensor(20460422., device='cuda:0'), tensor(20454886., device='cuda:0'), tensor(20427930., device='cuda:0'), tensor(20438668., device='cuda:0'), tensor(20435342., device='cuda:0'), tensor(20417202., device='cuda:0')]
Prediction loss: [tensor(14251432., device='cuda:0'), tensor(14208352., device='cuda:0'), tensor(14191173., device='cuda:0'), tensor(14247330., device='cuda:0'), tensor(14238959., device='cuda:0'), tensor(14230621., device='cuda:0'), tensor(14100877., device='cuda:0'), tensor(14145683., device='cuda:0'), tensor(14133974., device='cuda:0'), tensor(14166554., device='cuda:0')]
Others: [{'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': 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')}, {'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(2.6173e+11, device='cuda:0'), tensor(2.6033e+11, device='cuda:0'), tensor(2.6054e+11, device='cuda:0'), tensor(2.6136e+11, device='cuda:0'), tensor(2.6066e+11, device='cuda:0'), tensor(2.6134e+11, device='cuda:0'), tensor(2.5850e+11, device='cuda:0'), tensor(2.5963e+11, device='cuda:0'), tensor(2.5920e+11, device='cuda:0'), tensor(2.6033e+11, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=514845), datetime.timedelta(microseconds=596500), datetime.timedelta(microseconds=478943), datetime.timedelta(microseconds=520864), datetime.timedelta(microseconds=600486), datetime.timedelta(microseconds=563588), datetime.timedelta(microseconds=555673), datetime.timedelta(microseconds=562643), datetime.timedelta(microseconds=549697), datetime.timedelta(microseconds=560653)]
Phi time: [datetime.timedelta(microseconds=853424), datetime.timedelta(microseconds=874313), datetime.timedelta(microseconds=849679), datetime.timedelta(microseconds=861585), datetime.timedelta(microseconds=865635), datetime.timedelta(microseconds=848197), datetime.timedelta(microseconds=852782), datetime.timedelta(microseconds=844228), datetime.timedelta(microseconds=846749), datetime.timedelta(microseconds=843463)]
