Precision: [tensor(0.9992, device='cuda:0'), tensor(0.9988, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9985, device='cuda:0'), tensor(0.9985, device='cuda:0'), tensor(0.9987, device='cuda:0'), tensor(0.9988, device='cuda:0'), tensor(0.9988, device='cuda:0'), tensor(0.9990, device='cuda:0'), tensor(0.9988, device='cuda:0')]
Output distance: [tensor(29085.2148, device='cuda:0'), tensor(29051.1230, device='cuda:0'), tensor(29073.6289, device='cuda:0'), tensor(29175.2695, device='cuda:0'), tensor(29143.5391, device='cuda:0'), tensor(29078.7539, device='cuda:0'), tensor(29124.2148, device='cuda:0'), tensor(29149.9355, device='cuda:0'), tensor(29130.0879, device='cuda:0'), tensor(29101.1543, device='cuda:0')]
Prediction loss: [tensor(30705.3516, device='cuda:0'), tensor(32070.6445, device='cuda:0'), tensor(30291.8457, device='cuda:0'), tensor(30693., device='cuda:0'), tensor(32109.9121, device='cuda:0'), tensor(31257.4590, device='cuda:0'), tensor(31407.3145, device='cuda:0'), tensor(31729.8535, device='cuda:0'), tensor(31307.5918, device='cuda:0'), tensor(32340.3867, device='cuda:0')]
Others: [{'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': 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': 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')}]
Compressed training loss: [tensor(39054036., device='cuda:0'), tensor(40665152., device='cuda:0'), tensor(38920672., device='cuda:0'), tensor(39313704., device='cuda:0'), tensor(40283144., device='cuda:0'), tensor(39715104., device='cuda:0'), tensor(40086348., device='cuda:0'), tensor(40728608., device='cuda:0'), tensor(40087888., device='cuda:0'), tensor(41301636., device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=552680), datetime.timedelta(microseconds=479985), datetime.timedelta(microseconds=611431), datetime.timedelta(microseconds=550685), datetime.timedelta(microseconds=497853), datetime.timedelta(microseconds=608442), datetime.timedelta(microseconds=551683), datetime.timedelta(microseconds=557657), datetime.timedelta(microseconds=565623), datetime.timedelta(microseconds=576576)]
Phi time: [datetime.timedelta(microseconds=859225), datetime.timedelta(microseconds=852758), datetime.timedelta(microseconds=923586), datetime.timedelta(microseconds=864935), datetime.timedelta(microseconds=860190), datetime.timedelta(microseconds=908137), datetime.timedelta(microseconds=874736), datetime.timedelta(microseconds=874326), datetime.timedelta(microseconds=876571), datetime.timedelta(microseconds=871596)]
