Precision: [tensor(0.9624, device='cuda:0'), tensor(0.9605, device='cuda:0'), tensor(0.9609, device='cuda:0'), tensor(0.9605, device='cuda:0'), tensor(0.9595, device='cuda:0'), tensor(0.9607, device='cuda:0'), tensor(0.9572, device='cuda:0'), tensor(0.9602, device='cuda:0'), tensor(0.9567, device='cuda:0'), tensor(0.9613, device='cuda:0')]

Output distance: [tensor(104.5252, device='cuda:0'), tensor(106.4829, device='cuda:0'), tensor(107.1648, device='cuda:0'), tensor(109.9574, device='cuda:0'), tensor(106.8143, device='cuda:0'), tensor(107.5088, device='cuda:0'), tensor(113.2586, device='cuda:0'), tensor(110.5261, device='cuda:0'), tensor(119.2707, device='cuda:0'), tensor(105.5685, device='cuda:0')]

Prediction loss: [tensor(363.8879, device='cuda:0'), tensor(380.9385, device='cuda:0'), tensor(376.4580, device='cuda:0'), tensor(381.2281, device='cuda:0'), tensor(366.2661, device='cuda:0'), tensor(376.3838, device='cuda:0'), tensor(373.4436, device='cuda:0'), tensor(377.0464, device='cuda:0'), tensor(386.2636, device='cuda:0'), tensor(365.9439, device='cuda:0')]

Others: [{'iter_num': 13, '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': 13, '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': 13, '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': 13, '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': 13, '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')}]

Compressed training loss: [tensor(3471410.7500, device='cuda:0'), tensor(3626676.7500, device='cuda:0'), tensor(3596892.7500, device='cuda:0'), tensor(3649600.2500, device='cuda:0'), tensor(3493243.5000, device='cuda:0'), tensor(3590449., device='cuda:0'), tensor(3573510.7500, device='cuda:0'), tensor(3582289., device='cuda:0'), tensor(3697648.7500, device='cuda:0'), tensor(3497263., device='cuda:0')]

Training loss: 3594386.5

Prediction time: [datetime.timedelta(microseconds=762769), datetime.timedelta(microseconds=831474), datetime.timedelta(microseconds=795626), datetime.timedelta(microseconds=781686), datetime.timedelta(microseconds=783680), datetime.timedelta(microseconds=791643), datetime.timedelta(microseconds=809566), datetime.timedelta(microseconds=804587), datetime.timedelta(microseconds=803589), datetime.timedelta(microseconds=799611)]

Phi time: [datetime.timedelta(seconds=1, microseconds=391384), datetime.timedelta(microseconds=854876), datetime.timedelta(microseconds=792159), datetime.timedelta(microseconds=792840), datetime.timedelta(microseconds=805091), datetime.timedelta(microseconds=801176), datetime.timedelta(microseconds=798987), datetime.timedelta(microseconds=801050), datetime.timedelta(microseconds=803824), datetime.timedelta(microseconds=799588)]

