Precision: [tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0')]

Output distance: [tensor(38801.0117, device='cuda:0'), tensor(38613.9844, device='cuda:0'), tensor(38645.7422, device='cuda:0'), tensor(38920.3672, device='cuda:0'), tensor(38787.2930, device='cuda:0'), tensor(38789.4609, device='cuda:0'), tensor(38964.5195, device='cuda:0'), tensor(38805.9180, device='cuda:0'), tensor(38921.5898, device='cuda:0'), tensor(38776.8359, device='cuda:0')]

Prediction loss: [tensor(38298.1797, device='cuda:0'), tensor(38712.4297, device='cuda:0'), tensor(38403.7344, device='cuda:0'), tensor(40151.6953, device='cuda:0'), tensor(39061.5469, device='cuda:0'), tensor(39133.8516, device='cuda:0'), tensor(39617.2539, device='cuda:0'), tensor(38448.9062, device='cuda:0'), tensor(38705.1719, device='cuda:0'), tensor(39912.9766, device='cuda:0')]

Others: [{'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': 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')}, {'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')}]

Compressed training loss: [tensor(3530635., device='cuda:0'), tensor(3577761.2500, device='cuda:0'), tensor(3563370.5000, device='cuda:0'), tensor(3637926.2500, device='cuda:0'), tensor(3565145., device='cuda:0'), tensor(3580242.7500, device='cuda:0'), tensor(3604099.7500, device='cuda:0'), tensor(3540719.2500, device='cuda:0'), tensor(3539657.2500, device='cuda:0'), tensor(3604979.5000, device='cuda:0')]

Training loss: 3569689.0

Prediction time: [datetime.timedelta(microseconds=732892), datetime.timedelta(microseconds=689075), datetime.timedelta(microseconds=680114), datetime.timedelta(microseconds=743849), datetime.timedelta(microseconds=677128), datetime.timedelta(microseconds=678125), datetime.timedelta(microseconds=679121), datetime.timedelta(microseconds=676132), datetime.timedelta(microseconds=738868), datetime.timedelta(microseconds=678124)]

Phi time: [datetime.timedelta(seconds=1, microseconds=420854), datetime.timedelta(microseconds=895141), datetime.timedelta(microseconds=846213), datetime.timedelta(microseconds=843659), datetime.timedelta(microseconds=848453), datetime.timedelta(microseconds=850005), datetime.timedelta(microseconds=855666), datetime.timedelta(microseconds=848504), datetime.timedelta(microseconds=860645), datetime.timedelta(microseconds=847930)]

