Precision: [tensor(0.6821, device='cuda:0'), tensor(0.6860, device='cuda:0'), tensor(0.6910, device='cuda:0'), tensor(0.6907, device='cuda:0'), tensor(0.6842, device='cuda:0'), tensor(0.6810, device='cuda:0'), tensor(0.6889, device='cuda:0'), tensor(0.6800, device='cuda:0'), tensor(0.6894, device='cuda:0'), tensor(0.6894, device='cuda:0')]
Output distance: [tensor(4.9420, device='cuda:0'), tensor(4.9341, device='cuda:0'), tensor(4.9241, device='cuda:0'), tensor(4.9247, device='cuda:0'), tensor(4.9378, device='cuda:0'), tensor(4.9441, device='cuda:0'), tensor(4.9283, device='cuda:0'), tensor(4.9462, device='cuda:0'), tensor(4.9273, device='cuda:0'), tensor(4.9273, device='cuda:0')]
Prediction loss: [tensor(18173098., device='cuda:0'), tensor(20511652., device='cuda:0'), tensor(19525408., device='cuda:0'), tensor(17655894., device='cuda:0'), tensor(19304072., device='cuda:0'), tensor(19136518., device='cuda:0'), tensor(16762204., device='cuda:0'), tensor(18281488., device='cuda:0'), tensor(18727750., device='cuda:0'), tensor(19772186., device='cuda:0')]
Others: [{'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': 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': 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': 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': 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')}]
Compressed training loss: [tensor(40866.5625, device='cuda:0'), tensor(40807.6328, device='cuda:0'), tensor(40939.7891, device='cuda:0'), tensor(40744.0391, device='cuda:0'), tensor(40900.9023, device='cuda:0'), tensor(40762.8867, device='cuda:0'), tensor(40686.0469, device='cuda:0'), tensor(41019.3477, device='cuda:0'), tensor(40888.1172, device='cuda:0'), tensor(40851.1094, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(microseconds=87627), datetime.timedelta(microseconds=84639), datetime.timedelta(microseconds=78665), datetime.timedelta(microseconds=92605), datetime.timedelta(microseconds=90614), datetime.timedelta(microseconds=90613), datetime.timedelta(microseconds=86631), datetime.timedelta(microseconds=85634), datetime.timedelta(microseconds=84639), datetime.timedelta(microseconds=85634)]
Phi time: [datetime.timedelta(microseconds=347526), datetime.timedelta(microseconds=238988), datetime.timedelta(microseconds=243967), datetime.timedelta(microseconds=249942), datetime.timedelta(microseconds=241974), datetime.timedelta(microseconds=239984), datetime.timedelta(microseconds=238989), datetime.timedelta(microseconds=242972), datetime.timedelta(microseconds=239984), datetime.timedelta(microseconds=237994)]
