Precision: [tensor(0.6897, device='cuda:0'), tensor(0.6902, device='cuda:0'), tensor(0.6860, device='cuda:0'), tensor(0.6839, device='cuda:0'), tensor(0.6871, device='cuda:0'), tensor(0.6902, device='cuda:0'), tensor(0.6855, device='cuda:0'), tensor(0.6834, device='cuda:0'), tensor(0.6913, device='cuda:0'), tensor(0.6905, device='cuda:0')]

Output distance: [tensor(4.9268, device='cuda:0'), tensor(4.9257, device='cuda:0'), tensor(4.9341, device='cuda:0'), tensor(4.9383, device='cuda:0'), tensor(4.9320, device='cuda:0'), tensor(4.9257, device='cuda:0'), tensor(4.9352, device='cuda:0'), tensor(4.9394, device='cuda:0'), tensor(4.9236, device='cuda:0'), tensor(4.9252, device='cuda:0')]

Prediction loss: [tensor(18139604., device='cuda:0'), tensor(19067418., device='cuda:0'), tensor(19586496., device='cuda:0'), tensor(18361558., device='cuda:0'), tensor(18879256., device='cuda:0'), tensor(18127320., device='cuda:0'), tensor(18929652., device='cuda:0'), tensor(17629876., device='cuda:0'), tensor(17407854., device='cuda:0'), tensor(19211396., device='cuda:0')]

Others: [{'iter_num': 30, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(40927.5000, device='cuda:0'), tensor(40632.4297, device='cuda:0'), tensor(40881.2852, device='cuda:0'), tensor(40856.7500, device='cuda:0'), tensor(40732.0469, device='cuda:0'), tensor(40901.7734, device='cuda:0'), tensor(40776.0469, device='cuda:0'), tensor(40797.3906, device='cuda:0'), tensor(40715.3789, device='cuda:0'), tensor(40842.1289, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=118791), datetime.timedelta(seconds=1, microseconds=141662), datetime.timedelta(microseconds=982912), datetime.timedelta(microseconds=983621), datetime.timedelta(seconds=1, microseconds=4953), datetime.timedelta(microseconds=999445), datetime.timedelta(microseconds=982330), datetime.timedelta(microseconds=991625), datetime.timedelta(microseconds=998719), datetime.timedelta(microseconds=990117)]

Phi time: [datetime.timedelta(microseconds=231200), datetime.timedelta(microseconds=232078), datetime.timedelta(microseconds=246366), datetime.timedelta(microseconds=225330), datetime.timedelta(microseconds=227832), datetime.timedelta(microseconds=250643), datetime.timedelta(microseconds=230671), datetime.timedelta(microseconds=230673), datetime.timedelta(microseconds=244449), datetime.timedelta(microseconds=226481)]

