Precision: [tensor(0.8782, device='cuda:0'), tensor(0.8762, device='cuda:0'), tensor(0.8761, device='cuda:0'), tensor(0.8801, device='cuda:0'), tensor(0.8779, device='cuda:0'), tensor(0.8777, device='cuda:0'), tensor(0.8779, device='cuda:0'), tensor(0.8772, device='cuda:0'), tensor(0.8752, device='cuda:0'), tensor(0.8781, device='cuda:0')]
Output distance: [tensor(987.7877, device='cuda:0'), tensor(1012.2393, device='cuda:0'), tensor(1020.1880, device='cuda:0'), tensor(990.7194, device='cuda:0'), tensor(994.6339, device='cuda:0'), tensor(986.4877, device='cuda:0'), tensor(1006.0479, device='cuda:0'), tensor(1013.8634, device='cuda:0'), tensor(1016.9402, device='cuda:0'), tensor(989.7169, device='cuda:0')]
Prediction loss: [tensor(1639.3176, device='cuda:0'), tensor(1738.7474, device='cuda:0'), tensor(1709.5040, device='cuda:0'), tensor(1709.9152, device='cuda:0'), tensor(1727.8496, device='cuda:0'), tensor(1671.2355, device='cuda:0'), tensor(1735.8596, device='cuda:0'), tensor(1689.6534, device='cuda:0'), tensor(1689.9858, device='cuda:0'), tensor(1621.0731, device='cuda:0')]
Others: [{'iter_num': 9, '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': 9, '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': 9, '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': 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(18735388., device='cuda:0'), tensor(19950988., device='cuda:0'), tensor(19612430., device='cuda:0'), tensor(19605226., device='cuda:0'), tensor(19819926., device='cuda:0'), tensor(19111498., device='cuda:0'), tensor(19905844., device='cuda:0'), tensor(19301658., device='cuda:0'), tensor(19306064., device='cuda:0'), tensor(18570376., device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=681112), datetime.timedelta(microseconds=612453), datetime.timedelta(microseconds=677123), datetime.timedelta(microseconds=611408), datetime.timedelta(microseconds=595474), datetime.timedelta(microseconds=702064), datetime.timedelta(microseconds=607425), datetime.timedelta(microseconds=606430), datetime.timedelta(microseconds=695053), datetime.timedelta(microseconds=692061)]
Phi time: [datetime.timedelta(microseconds=864352), datetime.timedelta(microseconds=861672), datetime.timedelta(microseconds=863208), datetime.timedelta(microseconds=855899), datetime.timedelta(microseconds=860154), datetime.timedelta(microseconds=866799), datetime.timedelta(microseconds=867872), datetime.timedelta(microseconds=866279), datetime.timedelta(microseconds=864477), datetime.timedelta(microseconds=859034)]
