Precision: [tensor(0.2257, device='cuda:0'), tensor(0.2290, device='cuda:0'), tensor(0.2277, device='cuda:0'), tensor(0.2250, device='cuda:0'), tensor(0.2292, device='cuda:0'), tensor(0.2310, device='cuda:0'), tensor(0.2293, device='cuda:0'), tensor(0.2260, device='cuda:0'), tensor(0.2282, device='cuda:0'), tensor(0.2267, device='cuda:0')]
Output distance: [tensor(19543820., device='cuda:0'), tensor(19509928., device='cuda:0'), tensor(19516582., device='cuda:0'), tensor(19538610., device='cuda:0'), tensor(19515742., device='cuda:0'), tensor(19486700., device='cuda:0'), tensor(19510304., device='cuda:0'), tensor(19539226., device='cuda:0'), tensor(19539042., device='cuda:0'), tensor(19523794., device='cuda:0')]
Prediction loss: [tensor(13621053., device='cuda:0'), tensor(13545903., device='cuda:0'), tensor(13663501., device='cuda:0'), tensor(13550149., device='cuda:0'), tensor(13532067., device='cuda:0'), tensor(13518405., device='cuda:0'), tensor(13516149., device='cuda:0'), tensor(13569083., device='cuda:0'), tensor(13505913., device='cuda:0'), tensor(13598357., 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(2.4781e+11, device='cuda:0'), tensor(2.4633e+11, device='cuda:0'), tensor(2.4850e+11, device='cuda:0'), tensor(2.4640e+11, device='cuda:0'), tensor(2.4621e+11, device='cuda:0'), tensor(2.4593e+11, device='cuda:0'), tensor(2.4588e+11, device='cuda:0'), tensor(2.4702e+11, device='cuda:0'), tensor(2.4544e+11, device='cuda:0'), tensor(2.4731e+11, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=591485), datetime.timedelta(microseconds=562617), datetime.timedelta(microseconds=564559), datetime.timedelta(microseconds=566599), datetime.timedelta(microseconds=573571), datetime.timedelta(microseconds=571576), datetime.timedelta(microseconds=587508), datetime.timedelta(microseconds=582536), datetime.timedelta(microseconds=566596), datetime.timedelta(microseconds=569584)]
Phi time: [datetime.timedelta(microseconds=887448), datetime.timedelta(microseconds=858563), datetime.timedelta(microseconds=859474), datetime.timedelta(microseconds=860853), datetime.timedelta(microseconds=883730), datetime.timedelta(microseconds=855915), datetime.timedelta(microseconds=903084), datetime.timedelta(microseconds=858042), datetime.timedelta(microseconds=878967), datetime.timedelta(microseconds=858623)]
