Precision: [tensor(0.9995, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9988, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9990, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9995, device='cuda:0')]
Output distance: [tensor(69972.5625, device='cuda:0'), tensor(70021.7812, device='cuda:0'), tensor(70350., device='cuda:0'), tensor(70128.5938, device='cuda:0'), tensor(70197.6797, device='cuda:0'), tensor(69967.8047, device='cuda:0'), tensor(69933.5078, device='cuda:0'), tensor(69981.0078, device='cuda:0'), tensor(70018.5547, device='cuda:0'), tensor(70000.3516, device='cuda:0')]
Prediction loss: [tensor(73593.8906, device='cuda:0'), tensor(73322.0078, device='cuda:0'), tensor(74934.6641, device='cuda:0'), tensor(73333.5312, device='cuda:0'), tensor(75392.9297, device='cuda:0'), tensor(72849.6641, device='cuda:0'), tensor(73782.1953, device='cuda:0'), tensor(76155.9062, device='cuda:0'), tensor(73949.3828, device='cuda:0'), tensor(74571.8203, device='cuda:0')]
Others: [{'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]
Compressed training loss: [tensor(65318288., device='cuda:0'), tensor(64462872., device='cuda:0'), tensor(65315960., device='cuda:0'), tensor(64159708., device='cuda:0'), tensor(65126020., device='cuda:0'), tensor(64429940., device='cuda:0'), tensor(65151704., device='cuda:0'), tensor(67251144., device='cuda:0'), tensor(64972636., device='cuda:0'), tensor(65751472., device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=552652), datetime.timedelta(microseconds=572572), datetime.timedelta(microseconds=474939), datetime.timedelta(microseconds=596471), datetime.timedelta(microseconds=476978), datetime.timedelta(microseconds=561619), datetime.timedelta(microseconds=489922), datetime.timedelta(microseconds=526959), datetime.timedelta(microseconds=513752), datetime.timedelta(microseconds=533736)]
Phi time: [datetime.timedelta(microseconds=861243), datetime.timedelta(microseconds=866137), datetime.timedelta(microseconds=855356), datetime.timedelta(microseconds=882517), datetime.timedelta(microseconds=867379), datetime.timedelta(microseconds=858944), datetime.timedelta(microseconds=861414), datetime.timedelta(microseconds=885693), datetime.timedelta(microseconds=891826), datetime.timedelta(microseconds=883244)]
