Precision: [tensor(0.5504, device='cuda:0'), tensor(0.5524, device='cuda:0'), tensor(0.5501, device='cuda:0'), tensor(0.5527, device='cuda:0'), tensor(0.5505, device='cuda:0'), tensor(0.5497, device='cuda:0'), tensor(0.5539, device='cuda:0'), tensor(0.5467, device='cuda:0'), tensor(0.5512, device='cuda:0'), tensor(0.5512, device='cuda:0')]
Output distance: [tensor(5.0039, device='cuda:0'), tensor(4.9919, device='cuda:0'), tensor(5.0055, device='cuda:0'), tensor(4.9898, device='cuda:0'), tensor(5.0034, device='cuda:0'), tensor(5.0081, device='cuda:0'), tensor(4.9829, device='cuda:0'), tensor(5.0260, device='cuda:0'), tensor(4.9987, device='cuda:0'), tensor(4.9992, device='cuda:0')]
Prediction loss: [tensor(17721066., device='cuda:0'), tensor(18455492., device='cuda:0'), tensor(17509766., device='cuda:0'), tensor(18474940., device='cuda:0'), tensor(18762300., device='cuda:0'), tensor(19073512., device='cuda:0'), tensor(18792332., device='cuda:0'), tensor(18969742., device='cuda:0'), tensor(17848240., device='cuda:0'), tensor(18954480., 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': 11, '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': 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': 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': 11, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40750.8984, device='cuda:0'), tensor(40829.0547, device='cuda:0'), tensor(40849.7734, device='cuda:0'), tensor(40904.4102, device='cuda:0'), tensor(40843.4062, device='cuda:0'), tensor(40692.3984, device='cuda:0'), tensor(40755.8398, device='cuda:0'), tensor(40823.9688, device='cuda:0'), tensor(40783.1406, device='cuda:0'), tensor(40744.8711, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=11, microseconds=85100), datetime.timedelta(seconds=10, microseconds=976458), datetime.timedelta(seconds=13, microseconds=455069), datetime.timedelta(seconds=8, microseconds=500362), datetime.timedelta(seconds=10, microseconds=925255), datetime.timedelta(seconds=10, microseconds=977104), datetime.timedelta(seconds=8, microseconds=482499), datetime.timedelta(seconds=10, microseconds=881325), datetime.timedelta(seconds=8, microseconds=688624), datetime.timedelta(seconds=13, microseconds=486517)]
Phi time: [datetime.timedelta(microseconds=441071), datetime.timedelta(microseconds=453078), datetime.timedelta(microseconds=403289), datetime.timedelta(microseconds=451112), datetime.timedelta(microseconds=448121), datetime.timedelta(microseconds=421237), datetime.timedelta(microseconds=386443), datetime.timedelta(microseconds=441201), datetime.timedelta(microseconds=445135), datetime.timedelta(microseconds=440152)]
