Precision: [tensor(0.5343, device='cuda:0'), tensor(0.5336, device='cuda:0'), tensor(0.5357, device='cuda:0'), tensor(0.5338, device='cuda:0'), tensor(0.5338, device='cuda:0'), tensor(0.5342, device='cuda:0'), tensor(0.5355, device='cuda:0'), tensor(0.5350, device='cuda:0'), tensor(0.5329, device='cuda:0'), tensor(0.5331, device='cuda:0')]

Output distance: [tensor(5.1000, device='cuda:0'), tensor(5.1042, device='cuda:0'), tensor(5.0916, device='cuda:0'), tensor(5.1032, device='cuda:0'), tensor(5.1032, device='cuda:0'), tensor(5.1011, device='cuda:0'), tensor(5.0932, device='cuda:0'), tensor(5.0958, device='cuda:0'), tensor(5.1084, device='cuda:0'), tensor(5.1074, device='cuda:0')]

Prediction loss: [tensor(17691656., device='cuda:0'), tensor(17295190., device='cuda:0'), tensor(17913542., device='cuda:0'), tensor(19116842., device='cuda:0'), tensor(20083732., device='cuda:0'), tensor(18894966., device='cuda:0'), tensor(18431552., device='cuda:0'), tensor(17128716., device='cuda:0'), tensor(16395413., device='cuda:0'), tensor(18758402., device='cuda:0')]

Others: [{'iter_num': 11, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 13, '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': 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': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 13, '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': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(40860.9922, device='cuda:0'), tensor(40837.6953, device='cuda:0'), tensor(40654.8555, device='cuda:0'), tensor(40784.1719, device='cuda:0'), tensor(40860.2891, device='cuda:0'), tensor(40677.0586, device='cuda:0'), tensor(41066.3516, device='cuda:0'), tensor(40776.4688, device='cuda:0'), tensor(40729.9062, device='cuda:0'), tensor(40986.6875, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=33720), datetime.timedelta(seconds=1, microseconds=50232), datetime.timedelta(seconds=1, microseconds=45609), datetime.timedelta(seconds=1, microseconds=31569), datetime.timedelta(seconds=1, microseconds=2042), datetime.timedelta(seconds=1, microseconds=26226), datetime.timedelta(seconds=1, microseconds=47824), datetime.timedelta(seconds=1, microseconds=44396), datetime.timedelta(seconds=1, microseconds=29436), datetime.timedelta(seconds=1, microseconds=4653)]

Phi time: [datetime.timedelta(microseconds=199506), datetime.timedelta(microseconds=201184), datetime.timedelta(microseconds=195438), datetime.timedelta(microseconds=192688), datetime.timedelta(microseconds=199706), datetime.timedelta(microseconds=191428), datetime.timedelta(microseconds=198506), datetime.timedelta(microseconds=195107), datetime.timedelta(microseconds=199765), datetime.timedelta(microseconds=191340)]

