Precision: [tensor(0.9998, device='cuda:0'), tensor(0.9998, device='cuda:0'), tensor(0.9998, device='cuda:0'), tensor(0.9998, device='cuda:0'), tensor(0.9998, device='cuda:0'), tensor(0.9998, device='cuda:0'), tensor(0.9997, device='cuda:0'), tensor(0.9998, device='cuda:0'), tensor(0.9998, device='cuda:0'), tensor(0.9998, device='cuda:0')]

Output distance: [tensor(142019.7500, device='cuda:0'), tensor(142245.5625, device='cuda:0'), tensor(142038.8750, device='cuda:0'), tensor(142145.4219, device='cuda:0'), tensor(142190.9844, device='cuda:0'), tensor(142308.4062, device='cuda:0'), tensor(142410.5781, device='cuda:0'), tensor(141939.8594, device='cuda:0'), tensor(142074.7344, device='cuda:0'), tensor(141896.0469, device='cuda:0')]

Prediction loss: [tensor(140180.0312, device='cuda:0'), tensor(139318.7344, device='cuda:0'), tensor(140282.4844, device='cuda:0'), tensor(138951.7031, device='cuda:0'), tensor(138065.8906, device='cuda:0'), tensor(142479.5469, device='cuda:0'), tensor(137777.3281, device='cuda:0'), tensor(143225.3438, device='cuda:0'), tensor(135701.8906, device='cuda:0'), tensor(135267.2969, device='cuda:0')]

Others: [{'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(1.9235e+08, device='cuda:0'), tensor(1.9090e+08, device='cuda:0'), tensor(1.9277e+08, device='cuda:0'), tensor(1.9069e+08, device='cuda:0'), tensor(1.9106e+08, device='cuda:0'), tensor(1.9409e+08, device='cuda:0'), tensor(1.9043e+08, device='cuda:0'), tensor(1.9374e+08, device='cuda:0'), tensor(1.8931e+08, device='cuda:0'), tensor(1.8890e+08, device='cuda:0')]

Training loss: 191261280.0

Prediction time: [datetime.timedelta(microseconds=929088), datetime.timedelta(microseconds=948009), datetime.timedelta(microseconds=950001), datetime.timedelta(microseconds=946015), datetime.timedelta(microseconds=954979), datetime.timedelta(microseconds=949030), datetime.timedelta(seconds=1, microseconds=75473), datetime.timedelta(microseconds=948009), datetime.timedelta(microseconds=947012), datetime.timedelta(microseconds=948009)]

Phi time: [datetime.timedelta(seconds=1, microseconds=877301), datetime.timedelta(seconds=1, microseconds=252593), datetime.timedelta(seconds=1, microseconds=273645), datetime.timedelta(seconds=1, microseconds=267666), datetime.timedelta(seconds=1, microseconds=269042), datetime.timedelta(seconds=1, microseconds=319443), datetime.timedelta(seconds=1, microseconds=290180), datetime.timedelta(seconds=1, microseconds=266849), datetime.timedelta(seconds=1, microseconds=297835), datetime.timedelta(seconds=1, microseconds=263061)]

