Precision: [tensor(0.5035, device='cuda:0'), tensor(0.5056, device='cuda:0'), tensor(0.5004, device='cuda:0'), tensor(0.4961, device='cuda:0'), tensor(0.5012, device='cuda:0'), tensor(0.5016, device='cuda:0'), tensor(0.5006, device='cuda:0'), tensor(0.4990, device='cuda:0'), tensor(0.5026, device='cuda:0'), tensor(0.5000, device='cuda:0')]

Output distance: [tensor(5.2849, device='cuda:0'), tensor(5.2728, device='cuda:0'), tensor(5.3038, device='cuda:0'), tensor(5.3295, device='cuda:0'), tensor(5.2990, device='cuda:0'), tensor(5.2964, device='cuda:0'), tensor(5.3027, device='cuda:0'), tensor(5.3122, device='cuda:0'), tensor(5.2906, device='cuda:0'), tensor(5.3059, device='cuda:0')]

Prediction loss: [tensor(19064950., device='cuda:0'), tensor(22455880., device='cuda:0'), tensor(18803626., device='cuda:0'), tensor(20179882., device='cuda:0'), tensor(18897052., device='cuda:0'), tensor(21486648., device='cuda:0'), tensor(19745344., device='cuda:0'), tensor(18558366., device='cuda:0'), tensor(19154662., device='cuda:0'), tensor(18787158., 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': 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': 13, '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': 13, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(40928.4648, device='cuda:0'), tensor(41204.3086, device='cuda:0'), tensor(40946.4023, device='cuda:0'), tensor(40552.4688, device='cuda:0'), tensor(40714.8242, device='cuda:0'), tensor(40663.1094, device='cuda:0'), tensor(40760.7109, device='cuda:0'), tensor(40577.5469, device='cuda:0'), tensor(41059.8633, device='cuda:0'), tensor(40643.2070, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=44509), datetime.timedelta(seconds=1, microseconds=36920), datetime.timedelta(seconds=1, microseconds=37761), datetime.timedelta(seconds=1, microseconds=23024), datetime.timedelta(seconds=1, microseconds=52515), datetime.timedelta(seconds=1, microseconds=30257), datetime.timedelta(seconds=1, microseconds=33070), datetime.timedelta(seconds=1, microseconds=28403), datetime.timedelta(seconds=1, microseconds=42259), datetime.timedelta(seconds=1, microseconds=21002)]

Phi time: [datetime.timedelta(microseconds=180216), datetime.timedelta(microseconds=183266), datetime.timedelta(microseconds=171104), datetime.timedelta(microseconds=183065), datetime.timedelta(microseconds=180092), datetime.timedelta(microseconds=199795), datetime.timedelta(microseconds=179388), datetime.timedelta(microseconds=193791), datetime.timedelta(microseconds=169606), datetime.timedelta(microseconds=179691)]

