Precision: [tensor(0.8267, device='cuda:0'), tensor(0.8241, device='cuda:0'), tensor(0.8264, device='cuda:0'), tensor(0.8274, device='cuda:0'), tensor(0.8254, device='cuda:0'), tensor(0.8246, device='cuda:0'), tensor(0.8240, device='cuda:0'), tensor(0.8248, device='cuda:0'), tensor(0.8243, device='cuda:0'), tensor(0.8264, device='cuda:0')]

Output distance: [tensor(13806.5156, device='cuda:0'), tensor(14018.7334, device='cuda:0'), tensor(13800.2236, device='cuda:0'), tensor(13792.3936, device='cuda:0'), tensor(13929.5449, device='cuda:0'), tensor(13969.0840, device='cuda:0'), tensor(13973.2090, device='cuda:0'), tensor(13959.1504, device='cuda:0'), tensor(13983.0322, device='cuda:0'), tensor(13791.9736, device='cuda:0')]

Prediction loss: [tensor(10271.2852, device='cuda:0'), tensor(10502.6973, device='cuda:0'), tensor(10744.6826, device='cuda:0'), tensor(10462.7803, device='cuda:0'), tensor(10628.5039, device='cuda:0'), tensor(10292.9258, device='cuda:0'), tensor(10489.6729, device='cuda:0'), tensor(10494.2959, device='cuda:0'), tensor(10370.6650, device='cuda:0'), tensor(11100.3799, device='cuda:0')]

Others: [{'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(1.8704e+08, device='cuda:0'), tensor(1.9106e+08, device='cuda:0'), tensor(1.9486e+08, device='cuda:0'), tensor(1.9134e+08, device='cuda:0'), tensor(1.9416e+08, device='cuda:0'), tensor(1.8664e+08, device='cuda:0'), tensor(1.9054e+08, device='cuda:0'), tensor(1.9110e+08, device='cuda:0'), tensor(1.8900e+08, device='cuda:0'), tensor(2.0089e+08, device='cuda:0')]

Training loss: 191402256.0

Prediction time: [datetime.timedelta(microseconds=776705), datetime.timedelta(microseconds=881255), datetime.timedelta(microseconds=727914), datetime.timedelta(microseconds=875288), datetime.timedelta(microseconds=807576), datetime.timedelta(microseconds=797617), datetime.timedelta(microseconds=713971), datetime.timedelta(microseconds=804589), datetime.timedelta(microseconds=801602), datetime.timedelta(microseconds=717956)]

Phi time: [datetime.timedelta(seconds=1, microseconds=427745), datetime.timedelta(microseconds=853698), datetime.timedelta(microseconds=803307), datetime.timedelta(microseconds=797599), datetime.timedelta(microseconds=800795), datetime.timedelta(microseconds=800303), datetime.timedelta(microseconds=823754), datetime.timedelta(microseconds=803502), datetime.timedelta(microseconds=799816), datetime.timedelta(microseconds=802023)]

