Precision: [tensor(0.0859, device='cuda:0'), tensor(0.0845, device='cuda:0'), tensor(0.0836, device='cuda:0'), tensor(0.0626, device='cuda:0'), tensor(0.0437, device='cuda:0'), tensor(0.0617, device='cuda:0'), tensor(0.0789, device='cuda:0'), tensor(0.0599, device='cuda:0'), tensor(0.0691, device='cuda:0'), tensor(0.0431, device='cuda:0')]

Output distance: [tensor(19.8537, device='cuda:0'), tensor(19.8564, device='cuda:0'), tensor(19.8582, device='cuda:0'), tensor(19.9002, device='cuda:0'), tensor(19.9380, device='cuda:0'), tensor(19.9021, device='cuda:0'), tensor(19.8676, device='cuda:0'), tensor(19.9057, device='cuda:0'), tensor(19.8872, device='cuda:0'), tensor(19.9392, device='cuda:0')]

Prediction loss: [tensor(110.7138, device='cuda:0'), tensor(111.3236, device='cuda:0'), tensor(112.1453, device='cuda:0'), tensor(110.1969, device='cuda:0'), tensor(109.2463, device='cuda:0'), tensor(109.0712, device='cuda:0'), tensor(111.0768, device='cuda:0'), tensor(106.1539, device='cuda:0'), tensor(111.2219, device='cuda:0'), tensor(108.1794, device='cuda:0')]

Others: [{'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}]

Compressed training loss: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=5, microseconds=89415), datetime.timedelta(seconds=5, microseconds=49584), datetime.timedelta(seconds=5, microseconds=82449), datetime.timedelta(seconds=5, microseconds=53568), datetime.timedelta(seconds=5, microseconds=42610), datetime.timedelta(seconds=5, microseconds=75476), datetime.timedelta(seconds=5, microseconds=98378), datetime.timedelta(seconds=5, microseconds=66510), datetime.timedelta(seconds=5, microseconds=69500), datetime.timedelta(seconds=5, microseconds=33651)]

Phi time: [datetime.timedelta(seconds=4, microseconds=191804), datetime.timedelta(seconds=4, microseconds=219833), datetime.timedelta(seconds=4, microseconds=205925), datetime.timedelta(seconds=4, microseconds=218163), datetime.timedelta(seconds=4, microseconds=250307), datetime.timedelta(seconds=4, microseconds=216466), datetime.timedelta(seconds=4, microseconds=232380), datetime.timedelta(seconds=4, microseconds=270887), datetime.timedelta(seconds=4, microseconds=218375), datetime.timedelta(seconds=4, microseconds=221724)]

