Precision: [tensor(0.8564, device='cuda:0'), tensor(0.8577, device='cuda:0'), tensor(0.8586, device='cuda:0'), tensor(0.8573, device='cuda:0'), tensor(0.8578, device='cuda:0'), tensor(0.8552, device='cuda:0'), tensor(0.8577, device='cuda:0'), tensor(0.8578, device='cuda:0'), tensor(0.8578, device='cuda:0'), tensor(0.8577, device='cuda:0')]

Output distance: [tensor(547.8375, device='cuda:0'), tensor(541.9323, device='cuda:0'), tensor(537.2473, device='cuda:0'), tensor(546.0623, device='cuda:0'), tensor(542.7750, device='cuda:0'), tensor(557.8155, device='cuda:0'), tensor(536.9727, device='cuda:0'), tensor(542.5746, device='cuda:0'), tensor(541.9031, device='cuda:0'), tensor(543.9791, device='cuda:0')]

Prediction loss: [tensor(615.7567, device='cuda:0'), tensor(614.3384, device='cuda:0'), tensor(614.7050, device='cuda:0'), tensor(602.4864, device='cuda:0'), tensor(612.8398, device='cuda:0'), tensor(594.4078, device='cuda:0'), tensor(608.2060, device='cuda:0'), tensor(609.7983, device='cuda:0'), tensor(629.0522, device='cuda:0'), tensor(576.6578, device='cuda:0')]

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

Compressed training loss: [tensor(9076315., device='cuda:0'), tensor(9040769., device='cuda:0'), tensor(9057468., device='cuda:0'), tensor(8904093., device='cuda:0'), tensor(9048142., device='cuda:0'), tensor(8782858., device='cuda:0'), tensor(8931958., device='cuda:0'), tensor(8991739., device='cuda:0'), tensor(9231694., device='cuda:0'), tensor(8512164., device='cuda:0')]

Training loss: 8886910.0

Prediction time: [datetime.timedelta(microseconds=727908), datetime.timedelta(microseconds=764757), datetime.timedelta(microseconds=743846), datetime.timedelta(microseconds=740857), datetime.timedelta(microseconds=738867), datetime.timedelta(microseconds=744842), datetime.timedelta(microseconds=663188), datetime.timedelta(microseconds=744842), datetime.timedelta(microseconds=845414), datetime.timedelta(microseconds=751812)]

Phi time: [datetime.timedelta(seconds=1, microseconds=429178), datetime.timedelta(microseconds=871276), datetime.timedelta(microseconds=811065), datetime.timedelta(microseconds=813274), datetime.timedelta(microseconds=810414), datetime.timedelta(microseconds=801997), datetime.timedelta(microseconds=809168), datetime.timedelta(microseconds=807166), datetime.timedelta(microseconds=809320), datetime.timedelta(microseconds=801668)]

