Precision: [tensor(0.5514, device='cuda:0'), tensor(0.5507, device='cuda:0'), tensor(0.5514, device='cuda:0'), tensor(0.5537, device='cuda:0'), tensor(0.5555, device='cuda:0'), tensor(0.5491, device='cuda:0'), tensor(0.5466, device='cuda:0'), tensor(0.5523, device='cuda:0'), tensor(0.5491, device='cuda:0'), tensor(0.5533, device='cuda:0')]

Output distance: [tensor(4.9976, device='cuda:0'), tensor(5.0018, device='cuda:0'), tensor(4.9976, device='cuda:0'), tensor(4.9840, device='cuda:0'), tensor(4.9730, device='cuda:0'), tensor(5.0113, device='cuda:0'), tensor(5.0265, device='cuda:0'), tensor(4.9924, device='cuda:0'), tensor(5.0118, device='cuda:0'), tensor(4.9861, device='cuda:0')]

Prediction loss: [tensor(16861904., device='cuda:0'), tensor(19165404., device='cuda:0'), tensor(18063624., device='cuda:0'), tensor(18523622., device='cuda:0'), tensor(17684744., device='cuda:0'), tensor(19830976., device='cuda:0'), tensor(20898120., device='cuda:0'), tensor(19604356., device='cuda:0'), tensor(17729700., device='cuda:0'), tensor(19107042., device='cuda:0')]

Others: [{'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(40812.0625, device='cuda:0'), tensor(40791.6953, device='cuda:0'), tensor(40842.4805, device='cuda:0'), tensor(40938.9062, device='cuda:0'), tensor(40752.1719, device='cuda:0'), tensor(40980.5156, device='cuda:0'), tensor(40917.2148, device='cuda:0'), tensor(40905.9453, device='cuda:0'), tensor(40778.2344, device='cuda:0'), tensor(40731.8281, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=48980), datetime.timedelta(seconds=1, microseconds=48706), datetime.timedelta(seconds=1, microseconds=56319), datetime.timedelta(seconds=1, microseconds=59456), datetime.timedelta(seconds=1, microseconds=38790), datetime.timedelta(seconds=1, microseconds=39814), datetime.timedelta(seconds=1, microseconds=26922), datetime.timedelta(seconds=1, microseconds=20318), datetime.timedelta(seconds=1, microseconds=53259), datetime.timedelta(seconds=1, microseconds=80409)]

Phi time: [datetime.timedelta(microseconds=240493), datetime.timedelta(microseconds=229132), datetime.timedelta(microseconds=242990), datetime.timedelta(microseconds=243755), datetime.timedelta(microseconds=242155), datetime.timedelta(microseconds=224856), datetime.timedelta(microseconds=222210), datetime.timedelta(microseconds=217652), datetime.timedelta(microseconds=218436), datetime.timedelta(microseconds=242962)]

