Precision: [tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(0.9998, device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0')]

Output distance: [tensor(38992.2188, device='cuda:0'), tensor(39600.2109, device='cuda:0'), tensor(39742.2969, device='cuda:0'), tensor(39523.4219, device='cuda:0'), tensor(39601.6992, device='cuda:0'), tensor(39266.0430, device='cuda:0'), tensor(38740.4492, device='cuda:0'), tensor(39662.9258, device='cuda:0'), tensor(38667.0430, device='cuda:0'), tensor(39881.7070, device='cuda:0')]

Prediction loss: [tensor(38999.2031, device='cuda:0'), tensor(40439.7969, device='cuda:0'), tensor(39679.3477, device='cuda:0'), tensor(41088.4375, device='cuda:0'), tensor(39255.6133, device='cuda:0'), tensor(39102.8203, device='cuda:0'), tensor(36163.8867, device='cuda:0'), tensor(39255.9609, device='cuda:0'), tensor(38490.4180, device='cuda:0'), tensor(40837.7773, device='cuda:0')]

Others: [{'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 27, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 23, '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': 25, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(3534170., device='cuda:0'), tensor(3647157.2500, device='cuda:0'), tensor(3531167.2500, device='cuda:0'), tensor(3663365.7500, device='cuda:0'), tensor(3543731.7500, device='cuda:0'), tensor(3611222.5000, device='cuda:0'), tensor(3417984., device='cuda:0'), tensor(3512894., device='cuda:0'), tensor(3563415.7500, device='cuda:0'), tensor(3677193., device='cuda:0')]

Training loss: 3583952.25

Prediction time: [datetime.timedelta(seconds=1, microseconds=675947), datetime.timedelta(seconds=1, microseconds=573327), datetime.timedelta(seconds=1, microseconds=701782), datetime.timedelta(seconds=1, microseconds=676888), datetime.timedelta(seconds=1, microseconds=686846), datetime.timedelta(seconds=1, microseconds=371172), datetime.timedelta(microseconds=661196), datetime.timedelta(seconds=1, microseconds=475741), datetime.timedelta(microseconds=717954), datetime.timedelta(seconds=1, microseconds=699790)]

Phi time: [datetime.timedelta(seconds=1, microseconds=411171), datetime.timedelta(microseconds=904634), datetime.timedelta(microseconds=853903), datetime.timedelta(microseconds=856599), datetime.timedelta(microseconds=858462), datetime.timedelta(microseconds=847358), datetime.timedelta(microseconds=853972), datetime.timedelta(microseconds=851038), datetime.timedelta(microseconds=853923), datetime.timedelta(microseconds=852312)]

