Precision: [tensor(0.2200, device='cuda:0'), tensor(0.2157, device='cuda:0'), tensor(0.2175, device='cuda:0'), tensor(0.2190, device='cuda:0'), tensor(0.2203, device='cuda:0'), tensor(0.2175, device='cuda:0'), tensor(0.2198, device='cuda:0'), tensor(0.2168, device='cuda:0'), tensor(0.2193, device='cuda:0'), tensor(0.2140, device='cuda:0')]
Output distance: [tensor(19537954., device='cuda:0'), tensor(19574720., device='cuda:0'), tensor(19570684., device='cuda:0'), tensor(19548408., device='cuda:0'), tensor(19536994., device='cuda:0'), tensor(19566136., device='cuda:0'), tensor(19560190., device='cuda:0'), tensor(19566642., device='cuda:0'), tensor(19542784., device='cuda:0'), tensor(19591982., device='cuda:0')]
Prediction loss: [tensor(13631615., device='cuda:0'), tensor(13580322., device='cuda:0'), tensor(13660119., device='cuda:0'), tensor(13603448., device='cuda:0'), tensor(13615116., device='cuda:0'), tensor(13518762., device='cuda:0'), tensor(13636691., device='cuda:0'), tensor(13637094., device='cuda:0'), tensor(13523259., device='cuda:0'), tensor(13583939., 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': 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')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]
Compressed training loss: [tensor(2.4754e+11, device='cuda:0'), tensor(2.4662e+11, device='cuda:0'), tensor(2.4855e+11, device='cuda:0'), tensor(2.4746e+11, device='cuda:0'), tensor(2.4711e+11, device='cuda:0'), tensor(2.4584e+11, device='cuda:0'), tensor(2.4761e+11, device='cuda:0'), tensor(2.4775e+11, device='cuda:0'), tensor(2.4623e+11, device='cuda:0'), tensor(2.4654e+11, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=567594), datetime.timedelta(microseconds=550664), datetime.timedelta(microseconds=555645), datetime.timedelta(microseconds=561619), datetime.timedelta(microseconds=552659), datetime.timedelta(microseconds=563609), datetime.timedelta(microseconds=565598), datetime.timedelta(microseconds=560623), datetime.timedelta(microseconds=551660), datetime.timedelta(microseconds=567594)]
Phi time: [datetime.timedelta(microseconds=861273), datetime.timedelta(microseconds=857591), datetime.timedelta(microseconds=870452), datetime.timedelta(microseconds=865205), datetime.timedelta(microseconds=861405), datetime.timedelta(microseconds=864660), datetime.timedelta(microseconds=861751), datetime.timedelta(microseconds=898630), datetime.timedelta(microseconds=859775), datetime.timedelta(microseconds=863941)]
