Precision: [tensor(0.6897, device='cuda:0'), tensor(0.6878, device='cuda:0'), tensor(0.6860, device='cuda:0'), tensor(0.6918, device='cuda:0'), tensor(0.6865, device='cuda:0'), tensor(0.6855, device='cuda:0'), tensor(0.6860, device='cuda:0'), tensor(0.6844, device='cuda:0'), tensor(0.6842, device='cuda:0'), tensor(0.6928, device='cuda:0')]
Output distance: [tensor(4.9268, device='cuda:0'), tensor(4.9304, device='cuda:0'), tensor(4.9341, device='cuda:0'), tensor(4.9226, device='cuda:0'), tensor(4.9331, device='cuda:0'), tensor(4.9352, device='cuda:0'), tensor(4.9341, device='cuda:0'), tensor(4.9373, device='cuda:0'), tensor(4.9378, device='cuda:0'), tensor(4.9205, device='cuda:0')]
Prediction loss: [tensor(18910456., device='cuda:0'), tensor(19949510., device='cuda:0'), tensor(17819166., device='cuda:0'), tensor(19484982., device='cuda:0'), tensor(20888978., device='cuda:0'), tensor(17798822., device='cuda:0'), tensor(21485446., device='cuda:0'), tensor(17738420., device='cuda:0'), tensor(17831408., device='cuda:0'), tensor(18992604., 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(40868.1719, device='cuda:0'), tensor(40859.8945, device='cuda:0'), tensor(40926.8594, device='cuda:0'), tensor(40973.7461, device='cuda:0'), tensor(39868.2344, device='cuda:0'), tensor(40886.4141, device='cuda:0'), tensor(41427.4727, device='cuda:0'), tensor(40905.5859, device='cuda:0'), tensor(40851.4883, device='cuda:0'), tensor(40921.5117, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=5, microseconds=119306), datetime.timedelta(seconds=5, microseconds=356284), datetime.timedelta(seconds=5, microseconds=325414), datetime.timedelta(seconds=5, microseconds=181029), datetime.timedelta(seconds=5, microseconds=178040), datetime.timedelta(seconds=5, microseconds=306495), datetime.timedelta(seconds=5, microseconds=399102), datetime.timedelta(seconds=5, microseconds=153146), datetime.timedelta(seconds=5, microseconds=200946), datetime.timedelta(seconds=5, microseconds=304504)]
Phi time: [datetime.timedelta(microseconds=402294), datetime.timedelta(microseconds=418226), datetime.timedelta(microseconds=343544), datetime.timedelta(microseconds=393332), datetime.timedelta(microseconds=359474), datetime.timedelta(microseconds=368437), datetime.timedelta(microseconds=395322), datetime.timedelta(microseconds=272842), datetime.timedelta(microseconds=336569), datetime.timedelta(microseconds=335578)]
