Precision: [tensor(0.2030, device='cuda:0'), tensor(0.2003, device='cuda:0'), tensor(0.1962, device='cuda:0'), tensor(0.2037, device='cuda:0'), tensor(0.2047, device='cuda:0'), tensor(0.1983, device='cuda:0'), tensor(0.2010, device='cuda:0'), tensor(0.2042, device='cuda:0'), tensor(0.2022, device='cuda:0'), tensor(0.1993, device='cuda:0')]
Output distance: [tensor(19664670., device='cuda:0'), tensor(19689148., device='cuda:0'), tensor(19726142., device='cuda:0'), tensor(19654700., device='cuda:0'), tensor(19659016., device='cuda:0'), tensor(19706288., device='cuda:0'), tensor(19698788., device='cuda:0'), tensor(19668706., device='cuda:0'), tensor(19680456., device='cuda:0'), tensor(19711790., device='cuda:0')]
Prediction loss: [tensor(13682673., device='cuda:0'), tensor(13588649., device='cuda:0'), tensor(13718700., device='cuda:0'), tensor(13653747., device='cuda:0'), tensor(13627975., device='cuda:0'), tensor(13625553., device='cuda:0'), tensor(13599467., device='cuda:0'), tensor(13602492., device='cuda:0'), tensor(13626233., device='cuda:0'), tensor(13649614., 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(2.5091e+11, device='cuda:0'), tensor(2.4995e+11, device='cuda:0'), tensor(2.5211e+11, device='cuda:0'), tensor(2.5044e+11, device='cuda:0'), tensor(2.5099e+11, device='cuda:0'), tensor(2.5036e+11, device='cuda:0'), tensor(2.4938e+11, device='cuda:0'), tensor(2.4967e+11, device='cuda:0'), tensor(2.5008e+11, device='cuda:0'), tensor(2.5055e+11, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=569585), datetime.timedelta(microseconds=579543), datetime.timedelta(microseconds=582577), datetime.timedelta(microseconds=570579), datetime.timedelta(microseconds=567593), datetime.timedelta(microseconds=590545), datetime.timedelta(microseconds=563636), datetime.timedelta(microseconds=584525), datetime.timedelta(microseconds=570579), datetime.timedelta(microseconds=593483)]
Phi time: [datetime.timedelta(microseconds=900239), datetime.timedelta(microseconds=855603), datetime.timedelta(microseconds=863462), datetime.timedelta(microseconds=860144), datetime.timedelta(microseconds=856679), datetime.timedelta(microseconds=859784), datetime.timedelta(microseconds=862964), datetime.timedelta(microseconds=901778), datetime.timedelta(microseconds=861941), datetime.timedelta(microseconds=860271)]
