Precision: [tensor(0.9993, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9992, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9997, device='cuda:0'), tensor(0.9995, device='cuda:0')]

Output distance: [tensor(24960.7305, device='cuda:0'), tensor(24926.1152, device='cuda:0'), tensor(23703.1621, device='cuda:0'), tensor(23467.7832, device='cuda:0'), tensor(25287.6738, device='cuda:0'), tensor(23074.1758, device='cuda:0'), tensor(29194.9707, device='cuda:0'), tensor(25160.6406, device='cuda:0'), tensor(28867.4375, device='cuda:0'), tensor(23473.9414, device='cuda:0')]

Prediction loss: [tensor(25926.0020, device='cuda:0'), tensor(25681.6523, device='cuda:0'), tensor(24094.0391, device='cuda:0'), tensor(23564.7090, device='cuda:0'), tensor(26066.0508, device='cuda:0'), tensor(22859.1152, device='cuda:0'), tensor(31718.2285, device='cuda:0'), tensor(26001.6562, device='cuda:0'), tensor(31340.9863, device='cuda:0'), tensor(23458.9668, device='cuda:0')]

Others: [{'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': 29, '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': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 21, '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': 25, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(8954711., device='cuda:0'), tensor(8868334., device='cuda:0'), tensor(8950486., device='cuda:0'), tensor(8923422., device='cuda:0'), tensor(8872917., device='cuda:0'), tensor(8686596., device='cuda:0'), tensor(9216702., device='cuda:0'), tensor(9045031., device='cuda:0'), tensor(9073622., device='cuda:0'), tensor(8775525., device='cuda:0')]

Training loss: 8859459.0

Prediction time: [datetime.timedelta(seconds=1, microseconds=686847), datetime.timedelta(seconds=1, microseconds=724687), datetime.timedelta(seconds=1, microseconds=661951), datetime.timedelta(seconds=1, microseconds=461804), datetime.timedelta(seconds=1, microseconds=708753), datetime.timedelta(seconds=1, microseconds=301481), datetime.timedelta(seconds=1, microseconds=713733), datetime.timedelta(seconds=1, microseconds=692821), datetime.timedelta(seconds=1, microseconds=693803), datetime.timedelta(seconds=1, microseconds=485699)]

Phi time: [datetime.timedelta(seconds=1, microseconds=489864), datetime.timedelta(microseconds=909495), datetime.timedelta(microseconds=856334), datetime.timedelta(microseconds=855698), datetime.timedelta(microseconds=862636), datetime.timedelta(microseconds=861094), datetime.timedelta(microseconds=855945), datetime.timedelta(microseconds=853539), datetime.timedelta(microseconds=865901), datetime.timedelta(microseconds=858813)]

