Precision: [tensor(0.3845, device='cuda:0'), tensor(0.3791, device='cuda:0'), tensor(0.3848, device='cuda:0'), tensor(0.3561, device='cuda:0'), tensor(0.3683, device='cuda:0'), tensor(0.3856, device='cuda:0'), tensor(0.3767, device='cuda:0'), tensor(0.3670, device='cuda:0'), tensor(0.3888, device='cuda:0'), tensor(0.4111, device='cuda:0')]

Output distance: [tensor(19.2563, device='cuda:0'), tensor(19.2672, device='cuda:0'), tensor(19.2557, device='cuda:0'), tensor(19.3132, device='cuda:0'), tensor(19.2887, device='cuda:0'), tensor(19.2542, device='cuda:0'), tensor(19.2721, device='cuda:0'), tensor(19.2914, device='cuda:0'), tensor(19.2479, device='cuda:0'), tensor(19.2031, device='cuda:0')]

Prediction loss: [tensor(108.1787, device='cuda:0'), tensor(108.1162, device='cuda:0'), tensor(107.6545, device='cuda:0'), tensor(108.6653, device='cuda:0'), tensor(109.2481, device='cuda:0'), tensor(108.8260, device='cuda:0'), tensor(107.4450, device='cuda:0'), tensor(109.2043, device='cuda:0'), tensor(107.8394, device='cuda:0'), tensor(109.2670, device='cuda:0')]

Others: [{'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}]

Compressed training loss: [tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=2, microseconds=328127), datetime.timedelta(seconds=2, microseconds=314185), datetime.timedelta(seconds=2, microseconds=270374), datetime.timedelta(seconds=2, microseconds=315182), datetime.timedelta(seconds=2, microseconds=747345), datetime.timedelta(seconds=2, microseconds=528380), datetime.timedelta(seconds=2, microseconds=539338), datetime.timedelta(seconds=2, microseconds=720465), datetime.timedelta(seconds=2, microseconds=638827), datetime.timedelta(seconds=2, microseconds=554170)]

Phi time: [datetime.timedelta(seconds=126, microseconds=935621), datetime.timedelta(seconds=125, microseconds=527595), datetime.timedelta(seconds=125, microseconds=104973), datetime.timedelta(seconds=125, microseconds=725553), datetime.timedelta(seconds=140, microseconds=224907), datetime.timedelta(seconds=147, microseconds=826854), datetime.timedelta(seconds=144, microseconds=201216), datetime.timedelta(seconds=143, microseconds=915346), datetime.timedelta(seconds=147, microseconds=501653), datetime.timedelta(seconds=148, microseconds=640398)]

