Precision: [tensor(0.1244, device='cuda:0'), tensor(0.1262, device='cuda:0'), tensor(0.1256, device='cuda:0'), tensor(0.1239, device='cuda:0'), tensor(0.1242, device='cuda:0'), tensor(0.1247, device='cuda:0'), tensor(0.1235, device='cuda:0'), tensor(0.1238, device='cuda:0'), tensor(0.1256, device='cuda:0'), tensor(0.1234, device='cuda:0')]
Output distance: [tensor(20057556., device='cuda:0'), tensor(20034996., device='cuda:0'), tensor(20040582., device='cuda:0'), tensor(20059276., device='cuda:0'), tensor(20049750., device='cuda:0'), tensor(20050210., device='cuda:0'), tensor(20071834., device='cuda:0'), tensor(20062814., device='cuda:0'), tensor(20019136., device='cuda:0'), tensor(20069566., device='cuda:0')]
Prediction loss: [tensor(12393647., device='cuda:0'), tensor(12333175., device='cuda:0'), tensor(12443244., device='cuda:0'), tensor(12371557., device='cuda:0'), tensor(12324808., device='cuda:0'), tensor(12365641., device='cuda:0'), tensor(12421654., device='cuda:0'), tensor(12367658., device='cuda:0'), tensor(12370873., device='cuda:0'), tensor(12332684., device='cuda:0')]
Others: [{'iter_num': 9, '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': 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': 11, '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': 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')}]
Compressed training loss: [tensor(2.5082e+11, device='cuda:0'), tensor(2.4910e+11, device='cuda:0'), tensor(2.5141e+11, device='cuda:0'), tensor(2.4976e+11, device='cuda:0'), tensor(2.4858e+11, device='cuda:0'), tensor(2.4910e+11, device='cuda:0'), tensor(2.5125e+11, device='cuda:0'), tensor(2.4994e+11, device='cuda:0'), tensor(2.4988e+11, device='cuda:0'), tensor(2.4883e+11, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=683103), datetime.timedelta(microseconds=691122), datetime.timedelta(microseconds=693060), datetime.timedelta(microseconds=592488), datetime.timedelta(microseconds=688017), datetime.timedelta(microseconds=768740), datetime.timedelta(microseconds=731891), datetime.timedelta(microseconds=736924), datetime.timedelta(microseconds=594483), datetime.timedelta(microseconds=681111)]
Phi time: [datetime.timedelta(microseconds=909285), datetime.timedelta(microseconds=864426), datetime.timedelta(microseconds=863504), datetime.timedelta(microseconds=899691), datetime.timedelta(microseconds=860972), datetime.timedelta(microseconds=895268), datetime.timedelta(microseconds=880336), datetime.timedelta(microseconds=900016), datetime.timedelta(microseconds=858832), datetime.timedelta(microseconds=859043)]
