Precision: [tensor(0.9993, device='cuda:0'), tensor(0.9988, device='cuda:0'), tensor(0.9990, device='cuda:0'), tensor(0.9980, device='cuda:0'), tensor(0.9990, device='cuda:0'), tensor(0.9997, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9992, device='cuda:0'), tensor(0.9990, device='cuda:0'), tensor(0.9993, device='cuda:0')]

Output distance: [tensor(23593.4980, device='cuda:0'), tensor(23333.0605, device='cuda:0'), tensor(23197.9082, device='cuda:0'), tensor(23225.8086, device='cuda:0'), tensor(23414.5859, device='cuda:0'), tensor(23089.1621, device='cuda:0'), tensor(22996.3633, device='cuda:0'), tensor(23297.9375, device='cuda:0'), tensor(23700.8125, device='cuda:0'), tensor(23336.6816, device='cuda:0')]

Prediction loss: [tensor(21602.1621, device='cuda:0'), tensor(21980.8164, device='cuda:0'), tensor(20762.3379, device='cuda:0'), tensor(22575.1621, device='cuda:0'), tensor(19541.4941, device='cuda:0'), tensor(21329.5117, device='cuda:0'), tensor(22499.6895, device='cuda:0'), tensor(22302.2402, device='cuda:0'), tensor(22421.1836, device='cuda:0'), tensor(23247.3008, device='cuda:0')]

Others: [{'iter_num': 13, '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': 13, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, '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': 11, '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': 13, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(8906544., device='cuda:0'), tensor(8813901., device='cuda:0'), tensor(8463515., device='cuda:0'), tensor(8466166., device='cuda:0'), tensor(8203868., device='cuda:0'), tensor(8742366., device='cuda:0'), tensor(9103562., device='cuda:0'), tensor(8720585., device='cuda:0'), tensor(9042603., device='cuda:0'), tensor(9063106., device='cuda:0')]

Training loss: 8850417.0

Prediction time: [datetime.timedelta(microseconds=572594), datetime.timedelta(microseconds=578569), datetime.timedelta(microseconds=597490), datetime.timedelta(microseconds=593506), datetime.timedelta(microseconds=635333), datetime.timedelta(microseconds=598486), datetime.timedelta(microseconds=547699), datetime.timedelta(microseconds=552679), datetime.timedelta(microseconds=549691), datetime.timedelta(microseconds=596495)]

Phi time: [datetime.timedelta(seconds=1, microseconds=253717), datetime.timedelta(microseconds=735624), datetime.timedelta(microseconds=650095), datetime.timedelta(microseconds=655284), datetime.timedelta(microseconds=653750), datetime.timedelta(microseconds=654234), datetime.timedelta(microseconds=651040), datetime.timedelta(microseconds=655607), datetime.timedelta(microseconds=653950), datetime.timedelta(microseconds=656283)]

