Precision: [tensor(0.6813, device='cuda:0'), tensor(0.6905, device='cuda:0'), tensor(0.6836, device='cuda:0'), tensor(0.6818, device='cuda:0'), tensor(0.6839, device='cuda:0'), tensor(0.6831, device='cuda:0'), tensor(0.6871, device='cuda:0'), tensor(0.6852, device='cuda:0'), tensor(0.6913, device='cuda:0'), tensor(0.6808, device='cuda:0')]

Output distance: [tensor(4.9436, device='cuda:0'), tensor(4.9252, device='cuda:0'), tensor(4.9388, device='cuda:0'), tensor(4.9425, device='cuda:0'), tensor(4.9383, device='cuda:0'), tensor(4.9399, device='cuda:0'), tensor(4.9320, device='cuda:0'), tensor(4.9357, device='cuda:0'), tensor(4.9236, device='cuda:0'), tensor(4.9446, device='cuda:0')]

Prediction loss: [tensor(18388182., device='cuda:0'), tensor(18900794., device='cuda:0'), tensor(18716050., device='cuda:0'), tensor(19257150., device='cuda:0'), tensor(18934430., device='cuda:0'), tensor(18940770., device='cuda:0'), tensor(18955674., device='cuda:0'), tensor(18451746., device='cuda:0'), tensor(19014376., device='cuda:0'), tensor(18438854., 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(40781.1133, device='cuda:0'), tensor(40887.3555, device='cuda:0'), tensor(40893.9336, device='cuda:0'), tensor(40844.9102, device='cuda:0'), tensor(40795.8398, device='cuda:0'), tensor(40881.3789, device='cuda:0'), tensor(40968.5938, device='cuda:0'), tensor(40792.1719, device='cuda:0'), tensor(40864.5078, device='cuda:0'), tensor(40749.6914, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=67, microseconds=443769), datetime.timedelta(seconds=67, microseconds=312643), datetime.timedelta(seconds=66, microseconds=902147), datetime.timedelta(seconds=67, microseconds=307508), datetime.timedelta(seconds=67, microseconds=331375), datetime.timedelta(seconds=67, microseconds=350619), datetime.timedelta(seconds=67, microseconds=305984), datetime.timedelta(seconds=67, microseconds=388133), datetime.timedelta(seconds=67, microseconds=530926), datetime.timedelta(seconds=67, microseconds=266207)]

Phi time: [datetime.timedelta(microseconds=291392), datetime.timedelta(microseconds=301368), datetime.timedelta(microseconds=283238), datetime.timedelta(microseconds=451356), datetime.timedelta(microseconds=532256), datetime.timedelta(microseconds=519057), datetime.timedelta(microseconds=493453), datetime.timedelta(microseconds=510396), datetime.timedelta(microseconds=451371), datetime.timedelta(microseconds=510692)]

