Precision: [tensor(0.4271, device='cuda:0'), tensor(0.4219, device='cuda:0'), tensor(0.4266, device='cuda:0'), tensor(0.4240, device='cuda:0'), tensor(0.4474, device='cuda:0'), tensor(0.4243, device='cuda:0'), tensor(0.4148, device='cuda:0'), tensor(0.4426, device='cuda:0'), tensor(0.4172, device='cuda:0'), tensor(0.4481, device='cuda:0')]

Output distance: [tensor(5.4518, device='cuda:0'), tensor(5.4623, device='cuda:0'), tensor(5.4529, device='cuda:0'), tensor(5.4581, device='cuda:0'), tensor(5.4114, device='cuda:0'), tensor(5.4576, device='cuda:0'), tensor(5.4765, device='cuda:0'), tensor(5.4208, device='cuda:0'), tensor(5.4718, device='cuda:0'), tensor(5.4098, device='cuda:0')]

Prediction loss: [tensor(14834222., device='cuda:0'), tensor(18066650., device='cuda:0'), tensor(17094350., device='cuda:0'), tensor(15328778., device='cuda:0'), tensor(17750806., device='cuda:0'), tensor(16146740., device='cuda:0'), tensor(16490414., device='cuda:0'), tensor(20904024., device='cuda:0'), tensor(19765482., device='cuda:0'), tensor(21251028., device='cuda:0')]

Others: [{'iter_num': 30, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(41427.3164, device='cuda:0'), tensor(41220.0977, device='cuda:0'), tensor(41422.3750, device='cuda:0'), tensor(40840.3555, device='cuda:0'), tensor(41013.6172, device='cuda:0'), tensor(40571.8125, device='cuda:0'), tensor(40568.8984, device='cuda:0'), tensor(40185.9141, device='cuda:0'), tensor(41537.2969, device='cuda:0'), tensor(40547.9531, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=205924), datetime.timedelta(seconds=1, microseconds=146176), datetime.timedelta(seconds=1, microseconds=107339), datetime.timedelta(seconds=1, microseconds=117294), datetime.timedelta(seconds=1, microseconds=136217), datetime.timedelta(seconds=1, microseconds=105347), datetime.timedelta(seconds=1, microseconds=105348), datetime.timedelta(seconds=1, microseconds=126259), datetime.timedelta(seconds=1, microseconds=142192), datetime.timedelta(seconds=1, microseconds=111323)]

Phi time: [datetime.timedelta(microseconds=173270), datetime.timedelta(microseconds=176257), datetime.timedelta(microseconds=182232), datetime.timedelta(microseconds=176261), datetime.timedelta(microseconds=178250), datetime.timedelta(microseconds=181235), datetime.timedelta(microseconds=173270), datetime.timedelta(microseconds=177253), datetime.timedelta(microseconds=182233), datetime.timedelta(microseconds=182232)]

