Precision: [tensor(0.9987, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9985, device='cuda:0'), tensor(0.9988, device='cuda:0'), tensor(0.9973, device='cuda:0'), tensor(0.9987, device='cuda:0'), tensor(0.9983, device='cuda:0'), tensor(0.9990, device='cuda:0'), tensor(0.9985, device='cuda:0'), tensor(0.9992, device='cuda:0')]
Output distance: [tensor(29655.1816, device='cuda:0'), tensor(29729.9297, device='cuda:0'), tensor(29778.3203, device='cuda:0'), tensor(29816.3535, device='cuda:0'), tensor(29897.9961, device='cuda:0'), tensor(29773.4180, device='cuda:0'), tensor(29927.5469, device='cuda:0'), tensor(29735.8555, device='cuda:0'), tensor(29738.1113, device='cuda:0'), tensor(29677.7070, device='cuda:0')]
Prediction loss: [tensor(30515.8672, device='cuda:0'), tensor(31040.6348, device='cuda:0'), tensor(32234.6230, device='cuda:0'), tensor(31283.3789, device='cuda:0'), tensor(32705.1152, device='cuda:0'), tensor(33102.7852, device='cuda:0'), tensor(32522.4766, device='cuda:0'), tensor(31084.8555, device='cuda:0'), tensor(32263.1406, device='cuda:0'), tensor(32024.6504, 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': 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')}, {'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(38035960., device='cuda:0'), tensor(38902520., device='cuda:0'), tensor(40439384., device='cuda:0'), tensor(38894844., device='cuda:0'), tensor(40296948., device='cuda:0'), tensor(41178984., device='cuda:0'), tensor(40334604., device='cuda:0'), tensor(39105424., device='cuda:0'), tensor(40093304., device='cuda:0'), tensor(40130380., device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=568610), datetime.timedelta(microseconds=566619), datetime.timedelta(microseconds=484966), datetime.timedelta(microseconds=624378), datetime.timedelta(microseconds=485955), datetime.timedelta(microseconds=535748), datetime.timedelta(microseconds=627363), datetime.timedelta(microseconds=575581), datetime.timedelta(microseconds=568610), datetime.timedelta(microseconds=570602)]
Phi time: [datetime.timedelta(microseconds=882295), datetime.timedelta(microseconds=854947), datetime.timedelta(microseconds=862278), datetime.timedelta(microseconds=880388), datetime.timedelta(microseconds=890049), datetime.timedelta(microseconds=881242), datetime.timedelta(microseconds=874944), datetime.timedelta(microseconds=854294), datetime.timedelta(microseconds=853750), datetime.timedelta(microseconds=859767)]
