Precision: [tensor(0.6708, device='cuda:0'), tensor(0.6595, device='cuda:0'), tensor(0.6752, device='cuda:0'), tensor(0.6737, device='cuda:0'), tensor(0.6724, device='cuda:0'), tensor(0.6737, device='cuda:0'), tensor(0.6724, device='cuda:0'), tensor(0.6658, device='cuda:0'), tensor(0.6721, device='cuda:0'), tensor(0.6653, device='cuda:0')]

Output distance: [tensor(4.9646, device='cuda:0'), tensor(4.9871, device='cuda:0'), tensor(4.9556, device='cuda:0'), tensor(4.9588, device='cuda:0'), tensor(4.9614, device='cuda:0'), tensor(4.9588, device='cuda:0'), tensor(4.9614, device='cuda:0'), tensor(4.9745, device='cuda:0'), tensor(4.9619, device='cuda:0'), tensor(4.9756, device='cuda:0')]

Prediction loss: [tensor(18257802., device='cuda:0'), tensor(18945524., device='cuda:0'), tensor(18684704., device='cuda:0'), tensor(18000650., device='cuda:0'), tensor(20187794., device='cuda:0'), tensor(17349260., device='cuda:0'), tensor(17745646., device='cuda:0'), tensor(18400916., device='cuda:0'), tensor(18477796., device='cuda:0'), tensor(19737704., 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(40857.1914, device='cuda:0'), tensor(40924.3047, device='cuda:0'), tensor(40783.1523, device='cuda:0'), tensor(40751.3281, device='cuda:0'), tensor(40783.9570, device='cuda:0'), tensor(40700.7148, device='cuda:0'), tensor(40813.1133, device='cuda:0'), tensor(41007.4375, device='cuda:0'), tensor(40986.1914, device='cuda:0'), tensor(40945.0195, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=32, microseconds=864014), datetime.timedelta(seconds=33, microseconds=54264), datetime.timedelta(seconds=32, microseconds=919402), datetime.timedelta(seconds=32, microseconds=926051), datetime.timedelta(seconds=32, microseconds=930628), datetime.timedelta(seconds=32, microseconds=915472), datetime.timedelta(seconds=32, microseconds=997799), datetime.timedelta(seconds=33, microseconds=14827), datetime.timedelta(seconds=32, microseconds=815443), datetime.timedelta(seconds=32, microseconds=869167)]

Phi time: [datetime.timedelta(microseconds=206495), datetime.timedelta(microseconds=417446), datetime.timedelta(microseconds=418667), datetime.timedelta(microseconds=342383), datetime.timedelta(microseconds=356813), datetime.timedelta(microseconds=349909), datetime.timedelta(microseconds=382766), datetime.timedelta(microseconds=295151), datetime.timedelta(microseconds=415327), datetime.timedelta(microseconds=299902)]

