Precision: [tensor(0.1220, device='cuda:0'), tensor(0.1247, device='cuda:0'), tensor(0.1234, device='cuda:0'), tensor(0.1241, device='cuda:0'), tensor(0.1231, device='cuda:0'), tensor(0.1243, device='cuda:0'), tensor(0.1248, device='cuda:0'), tensor(0.1264, device='cuda:0'), tensor(0.1237, device='cuda:0'), tensor(0.1221, device='cuda:0')]
Output distance: [tensor(19974582., device='cuda:0'), tensor(19956032., device='cuda:0'), tensor(19944294., device='cuda:0'), tensor(19932942., device='cuda:0'), tensor(19962406., device='cuda:0'), tensor(19954206., device='cuda:0'), tensor(19937664., device='cuda:0'), tensor(19901218., device='cuda:0'), tensor(19946890., device='cuda:0'), tensor(19987026., device='cuda:0')]
Prediction loss: [tensor(12404026., device='cuda:0'), tensor(12435848., device='cuda:0'), tensor(12454706., device='cuda:0'), tensor(12393763., device='cuda:0'), tensor(12382503., device='cuda:0'), tensor(12433726., device='cuda:0'), tensor(12396675., device='cuda:0'), tensor(12402303., device='cuda:0'), tensor(12416512., device='cuda:0'), tensor(12371949., device='cuda:0')]
Others: [{'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': 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': 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(2.5062e+11, device='cuda:0'), tensor(2.5107e+11, device='cuda:0'), tensor(2.5198e+11, device='cuda:0'), tensor(2.5034e+11, device='cuda:0'), tensor(2.5043e+11, device='cuda:0'), tensor(2.5115e+11, device='cuda:0'), tensor(2.5089e+11, device='cuda:0'), tensor(2.5062e+11, device='cuda:0'), tensor(2.5118e+11, device='cuda:0'), tensor(2.5005e+11, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=691069), datetime.timedelta(microseconds=603443), datetime.timedelta(microseconds=692061), datetime.timedelta(microseconds=608410), datetime.timedelta(microseconds=683052), datetime.timedelta(microseconds=598464), datetime.timedelta(microseconds=595475), datetime.timedelta(microseconds=592436), datetime.timedelta(microseconds=608476), datetime.timedelta(microseconds=686089)]
Phi time: [datetime.timedelta(microseconds=888144), datetime.timedelta(microseconds=854577), datetime.timedelta(microseconds=864617), datetime.timedelta(microseconds=860332), datetime.timedelta(microseconds=865078), datetime.timedelta(microseconds=865572), datetime.timedelta(microseconds=866644), datetime.timedelta(microseconds=863863), datetime.timedelta(microseconds=863114), datetime.timedelta(microseconds=868271)]
