Precision: [tensor(0.8543, device='cuda:0'), tensor(0.8558, device='cuda:0'), tensor(0.8538, device='cuda:0'), tensor(0.8547, device='cuda:0'), tensor(0.8531, device='cuda:0'), tensor(0.8571, device='cuda:0'), tensor(0.8546, device='cuda:0'), tensor(0.8549, device='cuda:0'), tensor(0.8529, device='cuda:0'), tensor(0.8542, device='cuda:0')]

Output distance: [tensor(546.8546, device='cuda:0'), tensor(540.1106, device='cuda:0'), tensor(551.8940, device='cuda:0'), tensor(546.4069, device='cuda:0'), tensor(552.2688, device='cuda:0'), tensor(529.4662, device='cuda:0'), tensor(543.8934, device='cuda:0'), tensor(545.2658, device='cuda:0'), tensor(555.7239, device='cuda:0'), tensor(548.2584, device='cuda:0')]

Prediction loss: [tensor(621.4164, device='cuda:0'), tensor(607.4380, device='cuda:0'), tensor(590.5466, device='cuda:0'), tensor(603.7327, device='cuda:0'), tensor(603.9188, device='cuda:0'), tensor(584.3429, device='cuda:0'), tensor(601.5923, device='cuda:0'), tensor(602.6870, device='cuda:0'), tensor(606.0083, device='cuda:0'), tensor(608.7581, device='cuda:0')]

Others: [{'iter_num': 11, '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': 11, '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': 11, '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': 11, '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(9051417., device='cuda:0'), tensor(8886102., device='cuda:0'), tensor(8652294., device='cuda:0'), tensor(8818990., device='cuda:0'), tensor(8833613., device='cuda:0'), tensor(8553473., device='cuda:0'), tensor(8767856., device='cuda:0'), tensor(8817152., device='cuda:0'), tensor(8863336., device='cuda:0'), tensor(8900843., device='cuda:0')]

Training loss: 8827507.0

Prediction time: [datetime.timedelta(microseconds=735263), datetime.timedelta(microseconds=735299), datetime.timedelta(microseconds=802883), datetime.timedelta(microseconds=733344), datetime.timedelta(microseconds=745920), datetime.timedelta(microseconds=800420), datetime.timedelta(microseconds=714773), datetime.timedelta(microseconds=733469), datetime.timedelta(microseconds=734487), datetime.timedelta(microseconds=718024)]

Phi time: [datetime.timedelta(seconds=1, microseconds=330800), datetime.timedelta(microseconds=857031), datetime.timedelta(microseconds=852130), datetime.timedelta(microseconds=847793), datetime.timedelta(microseconds=826804), datetime.timedelta(microseconds=827746), datetime.timedelta(microseconds=835276), datetime.timedelta(microseconds=865594), datetime.timedelta(microseconds=852814), datetime.timedelta(microseconds=853858)]

