Precision: [tensor(0.3268, device='cuda:0'), tensor(0.3171, device='cuda:0'), tensor(0.3171, device='cuda:0'), tensor(0.0761, device='cuda:0'), tensor(0.2867, device='cuda:0'), tensor(0.2583, device='cuda:0'), tensor(0.2243, device='cuda:0'), tensor(0.2341, device='cuda:0'), tensor(0.3276, device='cuda:0'), tensor(0.2875, device='cuda:0')]

Output distance: [tensor(3.0592e+22, device='cuda:0'), tensor(7.6235e+22, device='cuda:0'), tensor(2.4000e+23, device='cuda:0'), tensor(6.6995e+23, device='cuda:0'), tensor(7.4716e+22, device='cuda:0'), tensor(5.6799e+22, device='cuda:0'), tensor(3.1893e+24, device='cuda:0'), tensor(2.0549e+23, device='cuda:0'), tensor(3.4715e+23, device='cuda:0'), tensor(4.4815e+22, device='cuda:0')]

Prediction loss: [tensor(5.0385e+22, device='cuda:0'), tensor(1.2636e+23, device='cuda:0'), tensor(4.8650e+23, device='cuda:0'), tensor(1.1812e+24, device='cuda:0'), tensor(1.2550e+23, device='cuda:0'), tensor(9.7873e+22, device='cuda:0'), tensor(5.8841e+24, device='cuda:0'), tensor(3.6100e+23, device='cuda:0'), tensor(6.1520e+23, device='cuda:0'), tensor(7.7248e+22, device='cuda:0')]

Others: [{'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(17999, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(3720418., device='cuda:0'), tensor(3536475.2500, device='cuda:0'), tensor(3660443.7500, device='cuda:0'), tensor(3834203.7500, device='cuda:0'), tensor(3739427.7500, device='cuda:0'), tensor(4030786., device='cuda:0'), tensor(3548295.5000, device='cuda:0'), tensor(3636131.2500, device='cuda:0'), tensor(3646771.5000, device='cuda:0'), tensor(3497263.7500, device='cuda:0')]

Training loss: 3597437.75

Prediction time: [datetime.timedelta(seconds=1, microseconds=336333), datetime.timedelta(seconds=1, microseconds=392095), datetime.timedelta(seconds=1, microseconds=358239), datetime.timedelta(seconds=1, microseconds=383134), datetime.timedelta(seconds=1, microseconds=388114), datetime.timedelta(seconds=1, microseconds=359235), datetime.timedelta(seconds=1, microseconds=392096), datetime.timedelta(seconds=1, microseconds=380146), datetime.timedelta(seconds=1, microseconds=374174), datetime.timedelta(seconds=1, microseconds=375168)]

Phi time: [datetime.timedelta(seconds=1, microseconds=258570), datetime.timedelta(microseconds=755052), datetime.timedelta(microseconds=678751), datetime.timedelta(microseconds=671885), datetime.timedelta(microseconds=669878), datetime.timedelta(microseconds=668310), datetime.timedelta(microseconds=669112), datetime.timedelta(microseconds=673678), datetime.timedelta(microseconds=667656), datetime.timedelta(microseconds=671132)]

