Precision: [tensor(0.9208, device='cuda:0'), tensor(0.9231, device='cuda:0'), tensor(0.9191, device='cuda:0'), tensor(0.9187, device='cuda:0'), tensor(0.9231, device='cuda:0'), tensor(0.9220, device='cuda:0'), tensor(0.9206, device='cuda:0'), tensor(0.9211, device='cuda:0'), tensor(0.9237, device='cuda:0'), tensor(0.9204, device='cuda:0')]
Output distance: [tensor(1671.6329, device='cuda:0'), tensor(1631.0515, device='cuda:0'), tensor(1715.8674, device='cuda:0'), tensor(1686.0017, device='cuda:0'), tensor(1628.1647, device='cuda:0'), tensor(1625.7170, device='cuda:0'), tensor(1639.4641, device='cuda:0'), tensor(1635.3113, device='cuda:0'), tensor(1584.0250, device='cuda:0'), tensor(1662.2100, device='cuda:0')]
Prediction loss: [tensor(4006.0574, device='cuda:0'), tensor(4178.2290, device='cuda:0'), tensor(4100.6401, device='cuda:0'), tensor(4092.6089, device='cuda:0'), tensor(4172.1289, device='cuda:0'), tensor(4087.7930, device='cuda:0'), tensor(4035.1047, device='cuda:0'), tensor(3977.8713, device='cuda:0'), tensor(4050.7847, device='cuda:0'), tensor(3969.1614, device='cuda:0')]
Others: [{'iter_num': 7, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]
Compressed training loss: [tensor(39367248., device='cuda:0'), tensor(41221336., device='cuda:0'), tensor(40362580., device='cuda:0'), tensor(40315244., device='cuda:0'), tensor(41224944., device='cuda:0'), tensor(40094284., device='cuda:0'), tensor(39634960., device='cuda:0'), tensor(39176012., device='cuda:0'), tensor(39812404., device='cuda:0'), tensor(38802376., device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=595549), datetime.timedelta(microseconds=604460), datetime.timedelta(microseconds=608441), datetime.timedelta(microseconds=600527), datetime.timedelta(microseconds=611481), datetime.timedelta(microseconds=589477), datetime.timedelta(microseconds=595499), datetime.timedelta(microseconds=766779), datetime.timedelta(microseconds=721915), datetime.timedelta(microseconds=691095)]
Phi time: [datetime.timedelta(microseconds=876395), datetime.timedelta(microseconds=857444), datetime.timedelta(microseconds=867780), datetime.timedelta(microseconds=857371), datetime.timedelta(microseconds=872751), datetime.timedelta(microseconds=859753), datetime.timedelta(microseconds=867979), datetime.timedelta(microseconds=859981), datetime.timedelta(microseconds=898780), datetime.timedelta(microseconds=865301)]
