Precision: [tensor(0.6821, device='cuda:0'), tensor(0.6773, device='cuda:0'), tensor(0.6831, device='cuda:0'), tensor(0.6721, device='cuda:0'), tensor(0.6784, device='cuda:0'), tensor(0.6794, device='cuda:0'), tensor(0.6829, device='cuda:0'), tensor(0.6773, device='cuda:0'), tensor(0.6787, device='cuda:0'), tensor(0.6742, device='cuda:0')]

Output distance: [tensor(4.9420, device='cuda:0'), tensor(4.9514, device='cuda:0'), tensor(4.9399, device='cuda:0'), tensor(4.9619, device='cuda:0'), tensor(4.9493, device='cuda:0'), tensor(4.9472, device='cuda:0'), tensor(4.9404, device='cuda:0'), tensor(4.9514, device='cuda:0'), tensor(4.9488, device='cuda:0'), tensor(4.9577, device='cuda:0')]

Prediction loss: [tensor(18924110., device='cuda:0'), tensor(16893752., device='cuda:0'), tensor(19000254., device='cuda:0'), tensor(18593428., device='cuda:0'), tensor(18517072., device='cuda:0'), tensor(19149674., device='cuda:0'), tensor(18726878., device='cuda:0'), tensor(19418040., device='cuda:0'), tensor(19246912., device='cuda:0'), tensor(18536188., device='cuda:0')]

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

Compressed training loss: [tensor(40850.2227, device='cuda:0'), tensor(40813.2109, device='cuda:0'), tensor(40682.9805, device='cuda:0'), tensor(40698.9062, device='cuda:0'), tensor(40814.8125, device='cuda:0'), tensor(40685.0977, device='cuda:0'), tensor(40701.4375, device='cuda:0'), tensor(40728.7656, device='cuda:0'), tensor(40858.5117, device='cuda:0'), tensor(41006.5234, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=39, microseconds=847693), datetime.timedelta(seconds=39, microseconds=842119), datetime.timedelta(seconds=39, microseconds=866539), datetime.timedelta(seconds=39, microseconds=888184), datetime.timedelta(seconds=39, microseconds=925977), datetime.timedelta(seconds=39, microseconds=951208), datetime.timedelta(seconds=40, microseconds=49747), datetime.timedelta(seconds=39, microseconds=829632), datetime.timedelta(seconds=39, microseconds=887201), datetime.timedelta(seconds=39, microseconds=925101)]

Phi time: [datetime.timedelta(microseconds=226607), datetime.timedelta(microseconds=254901), datetime.timedelta(microseconds=240759), datetime.timedelta(microseconds=344330), datetime.timedelta(microseconds=343954), datetime.timedelta(microseconds=331447), datetime.timedelta(microseconds=349968), datetime.timedelta(microseconds=235661), datetime.timedelta(microseconds=381118), datetime.timedelta(microseconds=348899)]

