Precision: [tensor(0.5458, device='cuda:0'), tensor(0.5472, device='cuda:0'), tensor(0.5469, device='cuda:0'), tensor(0.5447, device='cuda:0'), tensor(0.5461, device='cuda:0'), tensor(0.5460, device='cuda:0'), tensor(0.5430, device='cuda:0'), tensor(0.5462, device='cuda:0'), tensor(0.5453, device='cuda:0'), tensor(0.5468, device='cuda:0')]

Output distance: [tensor(5.0312, device='cuda:0'), tensor(5.0228, device='cuda:0'), tensor(5.0249, device='cuda:0'), tensor(5.0381, device='cuda:0'), tensor(5.0297, device='cuda:0'), tensor(5.0302, device='cuda:0'), tensor(5.0480, device='cuda:0'), tensor(5.0291, device='cuda:0'), tensor(5.0344, device='cuda:0'), tensor(5.0255, device='cuda:0')]

Prediction loss: [tensor(19394754., device='cuda:0'), tensor(18720042., device='cuda:0'), tensor(19346470., device='cuda:0'), tensor(18352486., device='cuda:0'), tensor(18283898., device='cuda:0'), tensor(17513596., device='cuda:0'), tensor(19078386., device='cuda:0'), tensor(18998306., device='cuda:0'), tensor(17989018., device='cuda:0'), tensor(18856300., device='cuda:0')]

Others: [{'iter_num': 30, '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': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, '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': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, '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': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(40777.4219, device='cuda:0'), tensor(40932.7734, device='cuda:0'), tensor(40876.6992, device='cuda:0'), tensor(40743.0234, device='cuda:0'), tensor(41030.7500, device='cuda:0'), tensor(40672.7773, device='cuda:0'), tensor(40906.6562, device='cuda:0'), tensor(40671.0703, device='cuda:0'), tensor(40787.2695, device='cuda:0'), tensor(40615.6797, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=212317), datetime.timedelta(seconds=1, microseconds=41346), datetime.timedelta(seconds=1, microseconds=179968), datetime.timedelta(seconds=1, microseconds=203078), datetime.timedelta(seconds=1, microseconds=27075), datetime.timedelta(seconds=1, microseconds=187622), datetime.timedelta(seconds=1, microseconds=222111), datetime.timedelta(seconds=1, microseconds=9886), datetime.timedelta(seconds=1, microseconds=205519), datetime.timedelta(seconds=1, microseconds=191180)]

Phi time: [datetime.timedelta(microseconds=207144), datetime.timedelta(microseconds=213778), datetime.timedelta(microseconds=225623), datetime.timedelta(microseconds=199371), datetime.timedelta(microseconds=221737), datetime.timedelta(microseconds=203554), datetime.timedelta(microseconds=205630), datetime.timedelta(microseconds=206715), datetime.timedelta(microseconds=197385), datetime.timedelta(microseconds=223301)]

