Precision: [tensor(0.9993, device='cuda:0'), tensor(0.9983, device='cuda:0'), tensor(0.9985, device='cuda:0'), tensor(0.9987, device='cuda:0'), tensor(0.9987, device='cuda:0'), tensor(0.9988, device='cuda:0'), tensor(0.9988, device='cuda:0'), tensor(0.9987, device='cuda:0'), tensor(0.9992, device='cuda:0'), tensor(0.9987, device='cuda:0')]
Output distance: [tensor(344829.4062, device='cuda:0'), tensor(345330.4375, device='cuda:0'), tensor(345352.7500, device='cuda:0'), tensor(346511.9062, device='cuda:0'), tensor(344747.2812, device='cuda:0'), tensor(348422.6562, device='cuda:0'), tensor(345934., device='cuda:0'), tensor(344697.6562, device='cuda:0'), tensor(344890.9062, device='cuda:0'), tensor(345632.0625, device='cuda:0')]
Prediction loss: [tensor(355858.7500, device='cuda:0'), tensor(348902.7812, device='cuda:0'), tensor(365644.1562, device='cuda:0'), tensor(359119.4688, device='cuda:0'), tensor(345617.6250, device='cuda:0'), tensor(351720., device='cuda:0'), tensor(351897.4375, device='cuda:0'), tensor(344456.2188, device='cuda:0'), tensor(343002.2812, device='cuda:0'), tensor(346638.2500, 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': 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': 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')}]
Compressed training loss: [tensor(2.1185e+08, device='cuda:0'), tensor(2.1001e+08, device='cuda:0'), tensor(2.1502e+08, device='cuda:0'), tensor(2.1832e+08, device='cuda:0'), tensor(2.0974e+08, device='cuda:0'), tensor(2.1084e+08, device='cuda:0'), tensor(2.1251e+08, device='cuda:0'), tensor(2.1303e+08, device='cuda:0'), tensor(2.1234e+08, device='cuda:0'), tensor(2.1349e+08, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=488951), datetime.timedelta(microseconds=605456), datetime.timedelta(microseconds=486954), datetime.timedelta(microseconds=579572), datetime.timedelta(microseconds=560674), datetime.timedelta(microseconds=553674), datetime.timedelta(microseconds=559648), datetime.timedelta(microseconds=555662), datetime.timedelta(microseconds=549691), datetime.timedelta(microseconds=568611)]
Phi time: [datetime.timedelta(microseconds=927684), datetime.timedelta(microseconds=888567), datetime.timedelta(microseconds=874826), datetime.timedelta(microseconds=883466), datetime.timedelta(microseconds=860572), datetime.timedelta(microseconds=863684), datetime.timedelta(microseconds=863741), datetime.timedelta(microseconds=865902), datetime.timedelta(microseconds=861339), datetime.timedelta(microseconds=864895)]
