Precision: [tensor(0.4909, device='cuda:0'), tensor(0.4749, device='cuda:0'), tensor(0.4854, device='cuda:0'), tensor(0.4938, device='cuda:0'), tensor(0.4920, device='cuda:0'), tensor(0.4867, device='cuda:0'), tensor(0.4854, device='cuda:0'), tensor(0.4726, device='cuda:0'), tensor(0.4875, device='cuda:0'), tensor(0.4844, device='cuda:0')]

Output distance: [tensor(5.3242, device='cuda:0'), tensor(5.3563, device='cuda:0'), tensor(5.3353, device='cuda:0'), tensor(5.3185, device='cuda:0'), tensor(5.3221, device='cuda:0'), tensor(5.3326, device='cuda:0'), tensor(5.3353, device='cuda:0'), tensor(5.3610, device='cuda:0'), tensor(5.3311, device='cuda:0'), tensor(5.3374, device='cuda:0')]

Prediction loss: [tensor(17134752., device='cuda:0'), tensor(19738738., device='cuda:0'), tensor(23825156., device='cuda:0'), tensor(17431610., device='cuda:0'), tensor(18892822., device='cuda:0'), tensor(14399990., device='cuda:0'), tensor(19398874., device='cuda:0'), tensor(21664120., device='cuda:0'), tensor(14087952., device='cuda:0'), tensor(14639188., device='cuda:0')]

Others: [{'iter_num': 9, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(40498.3125, device='cuda:0'), tensor(40888.6953, device='cuda:0'), tensor(40707.1367, device='cuda:0'), tensor(40679.7422, device='cuda:0'), tensor(41251.0859, device='cuda:0'), tensor(41192.7656, device='cuda:0'), tensor(40918.5977, device='cuda:0'), tensor(41163.0312, device='cuda:0'), tensor(40742.2227, device='cuda:0'), tensor(41373.3281, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=105306), datetime.timedelta(seconds=1, microseconds=110295), datetime.timedelta(seconds=1, microseconds=92373), datetime.timedelta(seconds=1, microseconds=78430), datetime.timedelta(seconds=1, microseconds=89373), datetime.timedelta(seconds=1, microseconds=30578), datetime.timedelta(seconds=1, microseconds=36605), datetime.timedelta(seconds=1, microseconds=92368), datetime.timedelta(seconds=1, microseconds=76439), datetime.timedelta(seconds=1, microseconds=79427)]

Phi time: [datetime.timedelta(microseconds=178247), datetime.timedelta(microseconds=189197), datetime.timedelta(microseconds=185209), datetime.timedelta(microseconds=180231), datetime.timedelta(microseconds=191191), datetime.timedelta(microseconds=180233), datetime.timedelta(microseconds=178240), datetime.timedelta(microseconds=177240), datetime.timedelta(microseconds=179287), datetime.timedelta(microseconds=195171)]

