Precision: [tensor(0.9987, device='cuda:0'), tensor(0.9980, device='cuda:0'), tensor(0.9975, device='cuda:0'), tensor(0.9978, device='cuda:0'), tensor(0.9982, device='cuda:0'), tensor(0.9983, device='cuda:0'), tensor(0.9982, device='cuda:0'), tensor(0.9983, device='cuda:0'), tensor(0.9980, device='cuda:0'), tensor(0.9980, device='cuda:0')]
Output distance: [tensor(349674.1875, device='cuda:0'), tensor(353607.0625, device='cuda:0'), tensor(350728.2188, device='cuda:0'), tensor(349976.2188, device='cuda:0'), tensor(349592.2812, device='cuda:0'), tensor(351368.2188, device='cuda:0'), tensor(349673.0312, device='cuda:0'), tensor(349758.6875, device='cuda:0'), tensor(349790.7188, device='cuda:0'), tensor(349655.8125, device='cuda:0')]
Prediction loss: [tensor(360645.9375, device='cuda:0'), tensor(348259.9688, device='cuda:0'), tensor(353593.2500, device='cuda:0'), tensor(370105.6250, device='cuda:0'), tensor(373270.3125, device='cuda:0'), tensor(350263.5000, device='cuda:0'), tensor(382333.1250, device='cuda:0'), tensor(366401.0625, device='cuda:0'), tensor(352451.1875, device='cuda:0'), tensor(350237.8750, device='cuda:0')]
Others: [{'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')}, {'iter_num': 5, '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': 5, '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(2.2037e+08, device='cuda:0'), tensor(2.0979e+08, device='cuda:0'), tensor(2.0859e+08, device='cuda:0'), tensor(2.1998e+08, device='cuda:0'), tensor(2.2017e+08, device='cuda:0'), tensor(2.1347e+08, device='cuda:0'), tensor(2.2284e+08, device='cuda:0'), tensor(2.1715e+08, device='cuda:0'), tensor(2.1349e+08, device='cuda:0'), tensor(2.1186e+08, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=474010), datetime.timedelta(microseconds=563580), datetime.timedelta(microseconds=549690), datetime.timedelta(microseconds=467980), datetime.timedelta(microseconds=525790), datetime.timedelta(microseconds=593506), datetime.timedelta(microseconds=475007), datetime.timedelta(microseconds=496913), datetime.timedelta(microseconds=604410), datetime.timedelta(microseconds=561639)]
Phi time: [datetime.timedelta(microseconds=868912), datetime.timedelta(microseconds=882454), datetime.timedelta(microseconds=857634), datetime.timedelta(microseconds=863970), datetime.timedelta(microseconds=885907), datetime.timedelta(microseconds=882209), datetime.timedelta(microseconds=866893), datetime.timedelta(microseconds=879832), datetime.timedelta(microseconds=893577), datetime.timedelta(microseconds=869240)]
