Precision: [tensor(0.1882, device='cuda:0'), tensor(0.1910, device='cuda:0'), tensor(0.1895, device='cuda:0'), tensor(0.1948, device='cuda:0'), tensor(0.1905, device='cuda:0'), tensor(0.1895, device='cuda:0'), tensor(0.1905, device='cuda:0'), tensor(0.1913, device='cuda:0'), tensor(0.1903, device='cuda:0'), tensor(0.1912, device='cuda:0')]
Output distance: [tensor(19764708., device='cuda:0'), tensor(19742036., device='cuda:0'), tensor(19756346., device='cuda:0'), tensor(19717436., device='cuda:0'), tensor(19748702., device='cuda:0'), tensor(19749802., device='cuda:0'), tensor(19758620., device='cuda:0'), tensor(19736830., device='cuda:0'), tensor(19756460., device='cuda:0'), tensor(19736680., device='cuda:0')]
Prediction loss: [tensor(13722729., device='cuda:0'), tensor(13702713., device='cuda:0'), tensor(13744488., device='cuda:0'), tensor(13703511., device='cuda:0'), tensor(13699929., device='cuda:0'), tensor(13747502., device='cuda:0'), tensor(13773388., device='cuda:0'), tensor(13766092., device='cuda:0'), tensor(13726253., device='cuda:0'), tensor(13654775., 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': 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')}]
Compressed training loss: [tensor(2.5084e+11, device='cuda:0'), tensor(2.5024e+11, device='cuda:0'), tensor(2.5079e+11, device='cuda:0'), tensor(2.4970e+11, device='cuda:0'), tensor(2.5001e+11, device='cuda:0'), tensor(2.5080e+11, device='cuda:0'), tensor(2.5104e+11, device='cuda:0'), tensor(2.5121e+11, device='cuda:0'), tensor(2.5015e+11, device='cuda:0'), tensor(2.4912e+11, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=546678), datetime.timedelta(microseconds=566596), datetime.timedelta(microseconds=550664), datetime.timedelta(microseconds=559622), datetime.timedelta(microseconds=552657), datetime.timedelta(microseconds=557635), datetime.timedelta(microseconds=548673), datetime.timedelta(microseconds=487929), datetime.timedelta(microseconds=592487), datetime.timedelta(microseconds=566596)]
Phi time: [datetime.timedelta(microseconds=870944), datetime.timedelta(microseconds=871011), datetime.timedelta(microseconds=858652), datetime.timedelta(microseconds=856179), datetime.timedelta(microseconds=874468), datetime.timedelta(microseconds=861821), datetime.timedelta(microseconds=862981), datetime.timedelta(microseconds=862689), datetime.timedelta(microseconds=889906), datetime.timedelta(microseconds=908489)]
