Precision: [tensor(0.2040, device='cuda:0'), tensor(0.2003, device='cuda:0'), tensor(0.2008, device='cuda:0'), tensor(0.2003, device='cuda:0'), tensor(0.1983, device='cuda:0'), tensor(0.2002, device='cuda:0'), tensor(0.2022, device='cuda:0'), tensor(0.2055, device='cuda:0'), tensor(0.2027, device='cuda:0'), tensor(0.2002, device='cuda:0')]
Output distance: [tensor(19723244., device='cuda:0'), tensor(19749770., device='cuda:0'), tensor(19752914., device='cuda:0'), tensor(19738084., device='cuda:0'), tensor(19773442., device='cuda:0'), tensor(19732430., device='cuda:0'), tensor(19737870., device='cuda:0'), tensor(19713640., device='cuda:0'), tensor(19728558., device='cuda:0'), tensor(19753426., device='cuda:0')]
Prediction loss: [tensor(13650717., device='cuda:0'), tensor(13635289., device='cuda:0'), tensor(13715494., device='cuda:0'), tensor(13670385., device='cuda:0'), tensor(13628114., device='cuda:0'), tensor(13684303., device='cuda:0'), tensor(13748755., device='cuda:0'), tensor(13633274., device='cuda:0'), tensor(13591297., device='cuda:0'), tensor(13602956., 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(2.4987e+11, device='cuda:0'), tensor(2.4972e+11, device='cuda:0'), tensor(2.5147e+11, device='cuda:0'), tensor(2.5034e+11, device='cuda:0'), tensor(2.4954e+11, device='cuda:0'), tensor(2.5053e+11, device='cuda:0'), tensor(2.5197e+11, device='cuda:0'), tensor(2.4963e+11, device='cuda:0'), tensor(2.4923e+11, device='cuda:0'), tensor(2.4941e+11, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=563614), datetime.timedelta(microseconds=577545), datetime.timedelta(microseconds=567592), datetime.timedelta(microseconds=577551), datetime.timedelta(microseconds=572576), datetime.timedelta(microseconds=572558), datetime.timedelta(microseconds=570572), datetime.timedelta(microseconds=560629), datetime.timedelta(microseconds=569585), datetime.timedelta(microseconds=583525)]
Phi time: [datetime.timedelta(microseconds=875619), datetime.timedelta(microseconds=862682), datetime.timedelta(microseconds=852558), datetime.timedelta(microseconds=859738), datetime.timedelta(microseconds=854535), datetime.timedelta(microseconds=857825), datetime.timedelta(microseconds=853223), datetime.timedelta(microseconds=869226), datetime.timedelta(microseconds=859691), datetime.timedelta(microseconds=863299)]
