Precision: [tensor(0.9270, device='cuda:0'), tensor(0.9247, device='cuda:0'), tensor(0.9227, device='cuda:0'), tensor(0.9288, device='cuda:0'), tensor(0.9244, device='cuda:0'), tensor(0.9225, device='cuda:0'), tensor(0.9253, device='cuda:0'), tensor(0.9241, device='cuda:0'), tensor(0.9284, device='cuda:0'), tensor(0.9213, device='cuda:0')]
Output distance: [tensor(9537.2646, device='cuda:0'), tensor(9734.2490, device='cuda:0'), tensor(9972.0742, device='cuda:0'), tensor(9236.2764, device='cuda:0'), tensor(9867.9170, device='cuda:0'), tensor(10222.2764, device='cuda:0'), tensor(9716.2295, device='cuda:0'), tensor(9922.5059, device='cuda:0'), tensor(9446.2285, device='cuda:0'), tensor(10190.9482, device='cuda:0')]
Prediction loss: [tensor(20510.3926, device='cuda:0'), tensor(21850.3340, device='cuda:0'), tensor(21471.4180, device='cuda:0'), tensor(21496.1602, device='cuda:0'), tensor(20794.5078, device='cuda:0'), tensor(20514.7148, device='cuda:0'), tensor(20999.7031, device='cuda:0'), tensor(21051.9453, device='cuda:0'), tensor(21077.0508, device='cuda:0'), tensor(21088.2344, device='cuda:0')]
Others: [{'iter_num': 7, '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': 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')}, {'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')}, {'iter_num': 7, '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': 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(2.0676e+08, device='cuda:0'), tensor(2.2060e+08, device='cuda:0'), tensor(2.1794e+08, device='cuda:0'), tensor(2.1699e+08, device='cuda:0'), tensor(2.0940e+08, device='cuda:0'), tensor(2.0617e+08, device='cuda:0'), tensor(2.1152e+08, device='cuda:0'), tensor(2.1251e+08, device='cuda:0'), tensor(2.1307e+08, device='cuda:0'), tensor(2.1404e+08, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=592488), datetime.timedelta(microseconds=589552), datetime.timedelta(microseconds=585516), datetime.timedelta(microseconds=680116), datetime.timedelta(microseconds=576555), datetime.timedelta(microseconds=657213), datetime.timedelta(microseconds=593484), datetime.timedelta(microseconds=583526), datetime.timedelta(microseconds=588503), datetime.timedelta(microseconds=662181)]
Phi time: [datetime.timedelta(microseconds=908143), datetime.timedelta(microseconds=864286), datetime.timedelta(microseconds=861333), datetime.timedelta(microseconds=851267), datetime.timedelta(microseconds=862737), datetime.timedelta(microseconds=854508), datetime.timedelta(microseconds=883708), datetime.timedelta(microseconds=865716), datetime.timedelta(microseconds=885986), datetime.timedelta(microseconds=854709)]
