Precision: [tensor(0.9997, device='cuda:0'), tensor(0.9997, device='cuda:0'), tensor(0.9997, device='cuda:0'), tensor(0.9998, device='cuda:0'), tensor(0.9998, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9997, device='cuda:0'), tensor(0.9998, device='cuda:0'), tensor(0.9998, device='cuda:0'), tensor(0.9990, device='cuda:0')]

Output distance: [tensor(23349.5977, device='cuda:0'), tensor(23252.3594, device='cuda:0'), tensor(23241.4082, device='cuda:0'), tensor(23337.0957, device='cuda:0'), tensor(23408.5996, device='cuda:0'), tensor(23419.3594, device='cuda:0'), tensor(23721.6738, device='cuda:0'), tensor(23369.8926, device='cuda:0'), tensor(23412.4473, device='cuda:0'), tensor(23430.3730, device='cuda:0')]

Prediction loss: [tensor(23081.8906, device='cuda:0'), tensor(21715.9180, device='cuda:0'), tensor(22009.2871, device='cuda:0'), tensor(23258.4316, device='cuda:0'), tensor(22009.8848, device='cuda:0'), tensor(23363.1152, device='cuda:0'), tensor(25154.0449, device='cuda:0'), tensor(23534.5137, device='cuda:0'), tensor(23426.7891, device='cuda:0'), tensor(23472.9727, device='cuda:0')]

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

Compressed training loss: [tensor(8964135., device='cuda:0'), tensor(8614234., device='cuda:0'), tensor(8829476., device='cuda:0'), tensor(8991258., device='cuda:0'), tensor(8814857., device='cuda:0'), tensor(8910777., device='cuda:0'), tensor(9232816., device='cuda:0'), tensor(9088952., device='cuda:0'), tensor(8920871., device='cuda:0'), tensor(8933280., device='cuda:0')]

Training loss: 8919483.0

Prediction time: [datetime.timedelta(microseconds=546686), datetime.timedelta(microseconds=637297), datetime.timedelta(microseconds=636302), datetime.timedelta(microseconds=580588), datetime.timedelta(microseconds=631322), datetime.timedelta(microseconds=633314), datetime.timedelta(microseconds=580537), datetime.timedelta(microseconds=631371), datetime.timedelta(microseconds=582530), datetime.timedelta(microseconds=637296)]

Phi time: [datetime.timedelta(seconds=1, microseconds=318727), datetime.timedelta(microseconds=791973), datetime.timedelta(microseconds=750361), datetime.timedelta(microseconds=724084), datetime.timedelta(microseconds=720879), datetime.timedelta(microseconds=719095), datetime.timedelta(microseconds=721503), datetime.timedelta(microseconds=726877), datetime.timedelta(microseconds=726752), datetime.timedelta(microseconds=724402)]

