Precision: [tensor(0.9995, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9997, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9995, device='cuda:0')]

Output distance: [tensor(23729.3164, device='cuda:0'), tensor(23408.6816, device='cuda:0'), tensor(23534.9375, device='cuda:0'), tensor(23501.6562, device='cuda:0'), tensor(23500.4004, device='cuda:0'), tensor(23696.4023, device='cuda:0'), tensor(23462.0312, device='cuda:0'), tensor(23497.6523, device='cuda:0'), tensor(23352.4160, device='cuda:0'), tensor(23426.7910, device='cuda:0')]

Prediction loss: [tensor(25254.4609, device='cuda:0'), tensor(23157.1543, device='cuda:0'), tensor(24975.7402, device='cuda:0'), tensor(24669.4531, device='cuda:0'), tensor(22966.0430, device='cuda:0'), tensor(22273.4883, device='cuda:0'), tensor(23376.1484, device='cuda:0'), tensor(23445.8848, device='cuda:0'), tensor(23279.2930, device='cuda:0'), tensor(24417.6289, device='cuda:0')]

Others: [{'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': 13, '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')}]

Compressed training loss: [tensor(9297446., device='cuda:0'), tensor(8799792., device='cuda:0'), tensor(9126448., device='cuda:0'), tensor(9138175., device='cuda:0'), tensor(8935408., device='cuda:0'), tensor(8761758., device='cuda:0'), tensor(8975742., device='cuda:0'), tensor(8896524., device='cuda:0'), tensor(8893492., device='cuda:0'), tensor(8941488., device='cuda:0')]

Training loss: 8875420.0

Prediction time: [datetime.timedelta(microseconds=526767), datetime.timedelta(microseconds=553652), datetime.timedelta(microseconds=557633), datetime.timedelta(microseconds=534733), datetime.timedelta(microseconds=606428), datetime.timedelta(microseconds=658208), datetime.timedelta(microseconds=537720), datetime.timedelta(microseconds=568589), datetime.timedelta(microseconds=565598), datetime.timedelta(microseconds=557636)]

Phi time: [datetime.timedelta(seconds=1, microseconds=325435), datetime.timedelta(microseconds=799541), datetime.timedelta(microseconds=729017), datetime.timedelta(microseconds=726953), datetime.timedelta(microseconds=731524), datetime.timedelta(microseconds=730691), datetime.timedelta(microseconds=749330), datetime.timedelta(microseconds=722373), datetime.timedelta(microseconds=734569), datetime.timedelta(microseconds=725590)]

