Precision: [tensor(0.4497, device='cuda:0'), tensor(0.4545, device='cuda:0'), tensor(0.4496, device='cuda:0'), tensor(0.4572, device='cuda:0'), tensor(0.4519, device='cuda:0'), tensor(0.4490, device='cuda:0'), tensor(0.4421, device='cuda:0'), tensor(0.4540, device='cuda:0'), tensor(0.4534, device='cuda:0'), tensor(0.4425, device='cuda:0')]

Output distance: [tensor(5.6078, device='cuda:0'), tensor(5.5789, device='cuda:0'), tensor(5.6083, device='cuda:0'), tensor(5.5631, device='cuda:0'), tensor(5.5946, device='cuda:0'), tensor(5.6120, device='cuda:0'), tensor(5.6535, device='cuda:0'), tensor(5.5820, device='cuda:0'), tensor(5.5857, device='cuda:0'), tensor(5.6514, device='cuda:0')]

Prediction loss: [tensor(19970578., device='cuda:0'), tensor(19667980., device='cuda:0'), tensor(17200518., device='cuda:0'), tensor(22229706., device='cuda:0'), tensor(17696498., device='cuda:0'), tensor(19207232., device='cuda:0'), tensor(19764320., device='cuda:0'), tensor(19138256., device='cuda:0'), tensor(20634436., device='cuda:0'), tensor(17314022., device='cuda:0')]

Others: [{'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': 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': 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': 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': 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(40881.5195, device='cuda:0'), tensor(40855.4453, device='cuda:0'), tensor(40894.6523, device='cuda:0'), tensor(40538.9570, device='cuda:0'), tensor(40703.0430, device='cuda:0'), tensor(40776.5859, device='cuda:0'), tensor(41274.1172, device='cuda:0'), tensor(40801.5664, device='cuda:0'), tensor(40590.5781, device='cuda:0'), tensor(40967.9141, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=175323), datetime.timedelta(seconds=1, microseconds=178916), datetime.timedelta(seconds=1, microseconds=154980), datetime.timedelta(seconds=1, microseconds=177396), datetime.timedelta(seconds=1, microseconds=177428), datetime.timedelta(seconds=1, microseconds=150220), datetime.timedelta(seconds=1, microseconds=143339), datetime.timedelta(seconds=1, microseconds=150190), datetime.timedelta(seconds=1, microseconds=142406), datetime.timedelta(seconds=1, microseconds=153228)]

Phi time: [datetime.timedelta(microseconds=179482), datetime.timedelta(microseconds=184597), datetime.timedelta(microseconds=195842), datetime.timedelta(microseconds=165302), datetime.timedelta(microseconds=165548), datetime.timedelta(microseconds=181179), datetime.timedelta(microseconds=172536), datetime.timedelta(microseconds=181071), datetime.timedelta(microseconds=180969), datetime.timedelta(microseconds=169496)]

