Precision: [tensor(0.1941, device='cuda:0'), tensor(0.1628, device='cuda:0'), tensor(0.2300, device='cuda:0'), tensor(0.2641, device='cuda:0'), tensor(0.1816, device='cuda:0'), tensor(0.1897, device='cuda:0'), tensor(0.2033, device='cuda:0'), tensor(0.1513, device='cuda:0'), tensor(0.1878, device='cuda:0'), tensor(0.2001, device='cuda:0')]

Output distance: [tensor(9.3088e+21, device='cuda:0'), tensor(1.8069e+25, device='cuda:0'), tensor(6.6068e+22, device='cuda:0'), tensor(5.2218e+22, device='cuda:0'), tensor(4.0103e+22, device='cuda:0'), tensor(6.2312e+22, device='cuda:0'), tensor(8.8803e+22, device='cuda:0'), tensor(1.0484e+22, device='cuda:0'), tensor(3.4308e+22, device='cuda:0'), tensor(5.6048e+22, device='cuda:0')]

Prediction loss: [tensor(1.5133e+22, device='cuda:0'), tensor(3.0988e+25, device='cuda:0'), tensor(1.1513e+23, device='cuda:0'), tensor(9.4986e+22, device='cuda:0'), tensor(6.8845e+22, device='cuda:0'), tensor(1.0451e+23, device='cuda:0'), tensor(1.5184e+23, device='cuda:0'), tensor(1.8613e+22, device='cuda:0'), tensor(5.8557e+22, device='cuda:0'), tensor(1.0056e+23, 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(17993, 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')}]

Compressed training loss: [tensor(9285030., device='cuda:0'), tensor(8720637., device='cuda:0'), tensor(9129962., device='cuda:0'), tensor(8733814., device='cuda:0'), tensor(8959877., device='cuda:0'), tensor(9318981., device='cuda:0'), tensor(9450923., device='cuda:0'), tensor(9018755., device='cuda:0'), tensor(8577594., device='cuda:0'), tensor(8558505., device='cuda:0')]

Training loss: 8875085.0

Prediction time: [datetime.timedelta(seconds=1, microseconds=324383), datetime.timedelta(seconds=1, microseconds=372178), datetime.timedelta(seconds=1, microseconds=345295), datetime.timedelta(seconds=1, microseconds=327370), datetime.timedelta(seconds=1, microseconds=357243), datetime.timedelta(seconds=1, microseconds=327322), datetime.timedelta(seconds=1, microseconds=341312), datetime.timedelta(seconds=1, microseconds=336330), datetime.timedelta(seconds=1, microseconds=371182), datetime.timedelta(seconds=1, microseconds=353263)]

Phi time: [datetime.timedelta(seconds=1, microseconds=239247), datetime.timedelta(microseconds=726626), datetime.timedelta(microseconds=645931), datetime.timedelta(microseconds=642212), datetime.timedelta(microseconds=643384), datetime.timedelta(microseconds=647027), datetime.timedelta(microseconds=648544), datetime.timedelta(microseconds=646408), datetime.timedelta(microseconds=669834), datetime.timedelta(microseconds=651653)]

