Precision: [tensor(0.2112, device='cuda:0'), tensor(0.2177, device='cuda:0'), tensor(0.2165, device='cuda:0'), tensor(0.2138, device='cuda:0'), tensor(0.2148, device='cuda:0'), tensor(0.2167, device='cuda:0'), tensor(0.2175, device='cuda:0'), tensor(0.2150, device='cuda:0'), tensor(0.2130, device='cuda:0'), tensor(0.2143, device='cuda:0')]
Output distance: [tensor(20493954., device='cuda:0'), tensor(20458010., device='cuda:0'), tensor(20457616., device='cuda:0'), tensor(20472226., device='cuda:0'), tensor(20471132., device='cuda:0'), tensor(20451604., device='cuda:0'), tensor(20445734., device='cuda:0'), tensor(20473322., device='cuda:0'), tensor(20484494., device='cuda:0'), tensor(20475586., device='cuda:0')]
Prediction loss: [tensor(14172698., device='cuda:0'), tensor(14198934., device='cuda:0'), tensor(14158094., device='cuda:0'), tensor(14139945., device='cuda:0'), tensor(14148912., device='cuda:0'), tensor(14189872., device='cuda:0'), tensor(14179163., device='cuda:0'), tensor(14188219., device='cuda:0'), tensor(14162977., device='cuda:0'), tensor(14224950., 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(2.6096e+11, device='cuda:0'), tensor(2.6017e+11, device='cuda:0'), tensor(2.6035e+11, device='cuda:0'), tensor(2.5981e+11, device='cuda:0'), tensor(2.6000e+11, device='cuda:0'), tensor(2.6032e+11, device='cuda:0'), tensor(2.6034e+11, device='cuda:0'), tensor(2.6043e+11, device='cuda:0'), tensor(2.6071e+11, device='cuda:0'), tensor(2.6139e+11, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=593512), datetime.timedelta(microseconds=613429), datetime.timedelta(microseconds=593513), datetime.timedelta(microseconds=591573), datetime.timedelta(microseconds=566598), datetime.timedelta(microseconds=550694), datetime.timedelta(microseconds=557664), datetime.timedelta(microseconds=556670), datetime.timedelta(microseconds=546710), datetime.timedelta(microseconds=560652)]
Phi time: [datetime.timedelta(seconds=1, microseconds=106483), datetime.timedelta(microseconds=938616), datetime.timedelta(microseconds=895760), datetime.timedelta(microseconds=912199), datetime.timedelta(microseconds=879954), datetime.timedelta(microseconds=849067), datetime.timedelta(microseconds=849054), datetime.timedelta(microseconds=845072), datetime.timedelta(microseconds=846430), datetime.timedelta(microseconds=850638)]
