Precision: [tensor(0.5409, device='cuda:0'), tensor(0.5417, device='cuda:0'), tensor(0.5467, device='cuda:0'), tensor(0.5414, device='cuda:0'), tensor(0.5411, device='cuda:0'), tensor(0.5441, device='cuda:0'), tensor(0.5470, device='cuda:0'), tensor(0.5473, device='cuda:0'), tensor(0.5459, device='cuda:0'), tensor(0.5411, device='cuda:0')]

Output distance: [tensor(5.0606, device='cuda:0'), tensor(5.0559, device='cuda:0'), tensor(5.0260, device='cuda:0'), tensor(5.0575, device='cuda:0'), tensor(5.0596, device='cuda:0'), tensor(5.0417, device='cuda:0'), tensor(5.0239, device='cuda:0'), tensor(5.0223, device='cuda:0'), tensor(5.0307, device='cuda:0'), tensor(5.0596, device='cuda:0')]

Prediction loss: [tensor(17912888., device='cuda:0'), tensor(18018932., device='cuda:0'), tensor(18398600., device='cuda:0'), tensor(18973086., device='cuda:0'), tensor(18110196., device='cuda:0'), tensor(19464790., device='cuda:0'), tensor(20163318., device='cuda:0'), tensor(19958358., device='cuda:0'), tensor(19384692., device='cuda:0'), tensor(17883608., 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(40871.6328, device='cuda:0'), tensor(40865.8477, device='cuda:0'), tensor(40867.0430, device='cuda:0'), tensor(40876.4766, device='cuda:0'), tensor(40894.1211, device='cuda:0'), tensor(40706.9062, device='cuda:0'), tensor(40819.8750, device='cuda:0'), tensor(40727.5078, device='cuda:0'), tensor(40813.3594, device='cuda:0'), tensor(40871.7891, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=54, microseconds=23918), datetime.timedelta(seconds=54, microseconds=132104), datetime.timedelta(seconds=53, microseconds=970475), datetime.timedelta(seconds=53, microseconds=922995), datetime.timedelta(seconds=53, microseconds=835806), datetime.timedelta(seconds=54, microseconds=43270), datetime.timedelta(seconds=54, microseconds=153267), datetime.timedelta(seconds=53, microseconds=708009), datetime.timedelta(seconds=53, microseconds=970785), datetime.timedelta(seconds=54, microseconds=100604)]

Phi time: [datetime.timedelta(microseconds=223981), datetime.timedelta(microseconds=352452), datetime.timedelta(microseconds=323845), datetime.timedelta(microseconds=249943), datetime.timedelta(microseconds=322465), datetime.timedelta(microseconds=225166), datetime.timedelta(microseconds=339257), datetime.timedelta(microseconds=342089), datetime.timedelta(microseconds=324768), datetime.timedelta(microseconds=250995)]

