Precision: [tensor(0.2780, device='cuda:0'), tensor(0.2873, device='cuda:0'), tensor(0.2798, device='cuda:0'), tensor(0.2845, device='cuda:0'), tensor(0.2864, device='cuda:0'), tensor(0.2876, device='cuda:0'), tensor(0.2821, device='cuda:0'), tensor(0.2870, device='cuda:0'), tensor(0.2837, device='cuda:0'), tensor(0.2842, device='cuda:0')]
Output distance: [tensor(6.6380, device='cuda:0'), tensor(6.5823, device='cuda:0'), tensor(6.6275, device='cuda:0'), tensor(6.5991, device='cuda:0'), tensor(6.5876, device='cuda:0'), tensor(6.5807, device='cuda:0'), tensor(6.6138, device='cuda:0'), tensor(6.5839, device='cuda:0'), tensor(6.6038, device='cuda:0'), tensor(6.6012, device='cuda:0')]
Prediction loss: [tensor(18775974., device='cuda:0'), tensor(16670578., device='cuda:0'), tensor(20021250., device='cuda:0'), tensor(19669040., device='cuda:0'), tensor(18099396., device='cuda:0'), tensor(17274164., device='cuda:0'), tensor(18176342., device='cuda:0'), tensor(18578180., device='cuda:0'), tensor(19519088., device='cuda:0'), tensor(20983774., device='cuda:0')]
Others: [{'iter_num': 7, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40751.1562, device='cuda:0'), tensor(40685.8672, device='cuda:0'), tensor(40733.4844, device='cuda:0'), tensor(40775.0703, device='cuda:0'), tensor(40819.1094, device='cuda:0'), tensor(40910.5312, device='cuda:0'), tensor(40901.0195, device='cuda:0'), tensor(40835.8906, device='cuda:0'), tensor(40850.4453, device='cuda:0'), tensor(40809.2148, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=8, microseconds=673216), datetime.timedelta(seconds=6, microseconds=366007), datetime.timedelta(seconds=6, microseconds=221784), datetime.timedelta(seconds=6, microseconds=239708), datetime.timedelta(seconds=6, microseconds=357213), datetime.timedelta(seconds=6, microseconds=413973), datetime.timedelta(seconds=6, microseconds=259623), datetime.timedelta(seconds=6, microseconds=288503), datetime.timedelta(seconds=6, microseconds=402024), datetime.timedelta(seconds=6, microseconds=364184)]
Phi time: [datetime.timedelta(microseconds=234008), datetime.timedelta(microseconds=383374), datetime.timedelta(microseconds=405292), datetime.timedelta(microseconds=356497), datetime.timedelta(microseconds=433175), datetime.timedelta(microseconds=415250), datetime.timedelta(microseconds=343553), datetime.timedelta(microseconds=426203), datetime.timedelta(microseconds=438153), datetime.timedelta(microseconds=373426)]
