Precision: [tensor(0.9990, device='cuda:0'), tensor(0.9992, device='cuda:0'), tensor(0.9987, device='cuda:0'), tensor(0.9985, device='cuda:0'), tensor(0.9987, device='cuda:0'), tensor(0.9987, device='cuda:0'), tensor(0.9980, device='cuda:0'), tensor(0.9988, device='cuda:0'), tensor(0.9990, device='cuda:0'), tensor(0.9983, device='cuda:0')]
Output distance: [tensor(29655.5625, device='cuda:0'), tensor(29669.1992, device='cuda:0'), tensor(29715.6094, device='cuda:0'), tensor(29770.2656, device='cuda:0'), tensor(29677.8496, device='cuda:0'), tensor(29646.2441, device='cuda:0'), tensor(29829.7949, device='cuda:0'), tensor(29703.0801, device='cuda:0'), tensor(29667.1699, device='cuda:0'), tensor(29679.2539, device='cuda:0')]
Prediction loss: [tensor(32021.6074, device='cuda:0'), tensor(31275.0879, device='cuda:0'), tensor(31548.6816, device='cuda:0'), tensor(32792.0469, device='cuda:0'), tensor(32216.0430, device='cuda:0'), tensor(31039.1992, device='cuda:0'), tensor(33557.9766, device='cuda:0'), tensor(31704.2559, device='cuda:0'), tensor(32057.8848, device='cuda:0'), tensor(31826.7578, device='cuda:0')]
Others: [{'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': 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': 5, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]
Compressed training loss: [tensor(39901348., device='cuda:0'), tensor(38992416., device='cuda:0'), tensor(40119384., device='cuda:0'), tensor(40628824., device='cuda:0'), tensor(39737804., device='cuda:0'), tensor(38871232., device='cuda:0'), tensor(41140256., device='cuda:0'), tensor(39842276., device='cuda:0'), tensor(39728456., device='cuda:0'), tensor(39886200., device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=559597), datetime.timedelta(microseconds=570601), datetime.timedelta(microseconds=558654), datetime.timedelta(microseconds=485958), datetime.timedelta(microseconds=490934), datetime.timedelta(microseconds=609432), datetime.timedelta(microseconds=488949), datetime.timedelta(microseconds=484967), datetime.timedelta(microseconds=603472), datetime.timedelta(microseconds=486953)]
Phi time: [datetime.timedelta(microseconds=889290), datetime.timedelta(microseconds=866306), datetime.timedelta(microseconds=854291), datetime.timedelta(microseconds=873266), datetime.timedelta(microseconds=856594), datetime.timedelta(microseconds=899795), datetime.timedelta(microseconds=872682), datetime.timedelta(microseconds=864369), datetime.timedelta(microseconds=881167), datetime.timedelta(microseconds=865086)]
