Precision: [tensor(0.5224, device='cuda:0'), tensor(0.5221, device='cuda:0'), tensor(0.5242, device='cuda:0'), tensor(0.5235, device='cuda:0'), tensor(0.5227, device='cuda:0'), tensor(0.5218, device='cuda:0'), tensor(0.5245, device='cuda:0'), tensor(0.5245, device='cuda:0'), tensor(0.5277, device='cuda:0'), tensor(0.5204, device='cuda:0')]

Output distance: [tensor(5.1714, device='cuda:0'), tensor(5.1735, device='cuda:0'), tensor(5.1609, device='cuda:0'), tensor(5.1651, device='cuda:0'), tensor(5.1699, device='cuda:0'), tensor(5.1751, device='cuda:0'), tensor(5.1588, device='cuda:0'), tensor(5.1594, device='cuda:0'), tensor(5.1399, device='cuda:0'), tensor(5.1835, device='cuda:0')]

Prediction loss: [tensor(17242832., device='cuda:0'), tensor(20064966., device='cuda:0'), tensor(16688184., device='cuda:0'), tensor(19814560., device='cuda:0'), tensor(19236506., device='cuda:0'), tensor(20885220., device='cuda:0'), tensor(19114568., device='cuda:0'), tensor(18309744., device='cuda:0'), tensor(17905138., device='cuda:0'), tensor(19175812., device='cuda:0')]

Others: [{'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(40844.2070, device='cuda:0'), tensor(41012.6953, device='cuda:0'), tensor(40669.8789, device='cuda:0'), tensor(40764.6562, device='cuda:0'), tensor(40894.0820, device='cuda:0'), tensor(40706.7578, device='cuda:0'), tensor(40731.9609, device='cuda:0'), tensor(40827.3008, device='cuda:0'), tensor(40813.1562, device='cuda:0'), tensor(40687.0547, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=42, microseconds=150446), datetime.timedelta(seconds=42, microseconds=507490), datetime.timedelta(seconds=42, microseconds=716327), datetime.timedelta(seconds=42, microseconds=262491), datetime.timedelta(seconds=42, microseconds=429656), datetime.timedelta(seconds=42, microseconds=812295), datetime.timedelta(seconds=42, microseconds=276701), datetime.timedelta(seconds=42, microseconds=522766), datetime.timedelta(seconds=42, microseconds=328819), datetime.timedelta(seconds=42, microseconds=654151)]

Phi time: [datetime.timedelta(microseconds=191390), datetime.timedelta(microseconds=340232), datetime.timedelta(microseconds=350952), datetime.timedelta(microseconds=367377), datetime.timedelta(microseconds=257834), datetime.timedelta(microseconds=413269), datetime.timedelta(microseconds=280144), datetime.timedelta(microseconds=393645), datetime.timedelta(microseconds=357495), datetime.timedelta(microseconds=346010)]

