Precision: [tensor(0.0015, device='cuda:0'), tensor(0.0017, device='cuda:0'), tensor(0.0009, device='cuda:0'), tensor(0.0016, device='cuda:0'), tensor(0.0012, device='cuda:0'), tensor(0.0015, device='cuda:0'), tensor(0.0010, device='cuda:0'), tensor(0.0016, device='cuda:0'), tensor(0.0013, device='cuda:0'), tensor(0.0010, device='cuda:0')]

Output distance: [tensor(22.0166, device='cuda:0'), tensor(22.0151, device='cuda:0'), tensor(22.0203, device='cuda:0'), tensor(22.0160, device='cuda:0'), tensor(22.0181, device='cuda:0'), tensor(22.0163, device='cuda:0'), tensor(22.0193, device='cuda:0'), tensor(22.0157, device='cuda:0'), tensor(22.0178, device='cuda:0'), tensor(22.0193, device='cuda:0')]

Prediction loss: [tensor(119.7695, device='cuda:0'), tensor(118.3442, device='cuda:0'), tensor(120.1872, device='cuda:0'), tensor(120.7116, device='cuda:0'), tensor(119.1252, device='cuda:0'), tensor(119.3238, device='cuda:0'), tensor(118.2603, device='cuda:0'), tensor(119.6735, device='cuda:0'), tensor(117.7009, device='cuda:0'), tensor(121.5655, device='cuda:0')]

Others: [{'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, 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=3, microseconds=488107), datetime.timedelta(seconds=3, microseconds=471698), datetime.timedelta(seconds=3, microseconds=477473), datetime.timedelta(seconds=3, microseconds=457605), datetime.timedelta(seconds=3, microseconds=528649), datetime.timedelta(seconds=3, microseconds=471237), datetime.timedelta(seconds=3, microseconds=473502), datetime.timedelta(seconds=3, microseconds=463954), datetime.timedelta(seconds=3, microseconds=467271), datetime.timedelta(seconds=3, microseconds=479489)]

Phi time: [datetime.timedelta(seconds=170, microseconds=747561), datetime.timedelta(seconds=170, microseconds=593843), datetime.timedelta(seconds=170, microseconds=659739), datetime.timedelta(seconds=170, microseconds=363882), datetime.timedelta(seconds=170, microseconds=750804), datetime.timedelta(seconds=170, microseconds=590152), datetime.timedelta(seconds=170, microseconds=55734), datetime.timedelta(seconds=170, microseconds=819462), datetime.timedelta(seconds=170, microseconds=433589), datetime.timedelta(seconds=170, microseconds=598081)]

