Precision: [tensor(0.8601, device='cuda:0'), tensor(0.8626, device='cuda:0'), tensor(0.8622, device='cuda:0'), tensor(0.8617, device='cuda:0'), tensor(0.8595, device='cuda:0'), tensor(0.8609, device='cuda:0'), tensor(0.8603, device='cuda:0'), tensor(0.8588, device='cuda:0'), tensor(0.8620, device='cuda:0'), tensor(0.8572, device='cuda:0')]

Output distance: [tensor(528.3973, device='cuda:0'), tensor(513.7813, device='cuda:0'), tensor(517.2242, device='cuda:0'), tensor(519.7192, device='cuda:0'), tensor(533.5208, device='cuda:0'), tensor(523.4009, device='cuda:0'), tensor(523.4624, device='cuda:0'), tensor(535.0808, device='cuda:0'), tensor(517.5993, device='cuda:0'), tensor(540.3896, device='cuda:0')]

Prediction loss: [tensor(611.2501, device='cuda:0'), tensor(588.2770, device='cuda:0'), tensor(612.6866, device='cuda:0'), tensor(615.2675, device='cuda:0'), tensor(597.3344, device='cuda:0'), tensor(625.0925, device='cuda:0'), tensor(633.7942, device='cuda:0'), tensor(601.0685, device='cuda:0'), tensor(592.9106, device='cuda:0'), tensor(616.8593, device='cuda:0')]

Others: [{'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, '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': 11, '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': 11, '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': 11, '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': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(9010044., device='cuda:0'), tensor(8688926., device='cuda:0'), tensor(9023297., device='cuda:0'), tensor(9049072., device='cuda:0'), tensor(8821057., device='cuda:0'), tensor(9193159., device='cuda:0'), tensor(9300977., device='cuda:0'), tensor(8882629., device='cuda:0'), tensor(8758776., device='cuda:0'), tensor(9104157., device='cuda:0')]

Training loss: 8919223.0

Prediction time: [datetime.timedelta(microseconds=758782), datetime.timedelta(microseconds=856368), datetime.timedelta(microseconds=780689), datetime.timedelta(microseconds=772723), datetime.timedelta(microseconds=761770), datetime.timedelta(microseconds=776703), datetime.timedelta(microseconds=779694), datetime.timedelta(microseconds=793633), datetime.timedelta(microseconds=771727), datetime.timedelta(microseconds=770730)]

Phi time: [datetime.timedelta(seconds=1, microseconds=381669), datetime.timedelta(microseconds=887688), datetime.timedelta(microseconds=853486), datetime.timedelta(microseconds=852863), datetime.timedelta(microseconds=854359), datetime.timedelta(microseconds=858025), datetime.timedelta(microseconds=860215), datetime.timedelta(microseconds=873730), datetime.timedelta(microseconds=853414), datetime.timedelta(microseconds=863085)]

