Precision: [tensor(0.8751, device='cuda:0'), tensor(0.8808, device='cuda:0'), tensor(0.8772, device='cuda:0'), tensor(0.8746, device='cuda:0'), tensor(0.8822, device='cuda:0'), tensor(0.8746, device='cuda:0'), tensor(0.8743, device='cuda:0'), tensor(0.8768, device='cuda:0'), tensor(0.8774, device='cuda:0'), tensor(0.8784, device='cuda:0')]
Output distance: [tensor(1001.1645, device='cuda:0'), tensor(974.5634, device='cuda:0'), tensor(996.1244, device='cuda:0'), tensor(1014.9086, device='cuda:0'), tensor(959.8711, device='cuda:0'), tensor(1007.8693, device='cuda:0'), tensor(1011.2485, device='cuda:0'), tensor(975.2064, device='cuda:0'), tensor(1003.1392, device='cuda:0'), tensor(984.5336, device='cuda:0')]
Prediction loss: [tensor(1761.9905, device='cuda:0'), tensor(1707.6067, device='cuda:0'), tensor(1742.4119, device='cuda:0'), tensor(1782.8573, device='cuda:0'), tensor(1761.3231, device='cuda:0'), tensor(1745.2487, device='cuda:0'), tensor(1725.6382, device='cuda:0'), tensor(1798.9014, device='cuda:0'), tensor(1672.8473, device='cuda:0'), tensor(1736.5652, 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': 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')}]
Compressed training loss: [tensor(19989382., device='cuda:0'), tensor(19218634., device='cuda:0'), tensor(19651334., device='cuda:0'), tensor(20226746., device='cuda:0'), tensor(19848888., device='cuda:0'), tensor(19719602., device='cuda:0'), tensor(19456168., device='cuda:0'), tensor(20255142., device='cuda:0'), tensor(18880340., device='cuda:0'), tensor(19524278., device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=601448), datetime.timedelta(microseconds=595475), datetime.timedelta(microseconds=610412), datetime.timedelta(microseconds=606429), datetime.timedelta(microseconds=606428), datetime.timedelta(microseconds=596468), datetime.timedelta(microseconds=595475), datetime.timedelta(microseconds=598440), datetime.timedelta(microseconds=595480), datetime.timedelta(microseconds=599459)]
Phi time: [datetime.timedelta(microseconds=878365), datetime.timedelta(microseconds=865071), datetime.timedelta(microseconds=855800), datetime.timedelta(microseconds=862860), datetime.timedelta(microseconds=856108), datetime.timedelta(microseconds=894247), datetime.timedelta(microseconds=857011), datetime.timedelta(microseconds=861273), datetime.timedelta(microseconds=855528), datetime.timedelta(microseconds=861177)]
