Precision: [tensor(0.9615, device='cuda:0'), tensor(0.9602, device='cuda:0'), tensor(0.9619, device='cuda:0'), tensor(0.9591, device='cuda:0'), tensor(0.9611, device='cuda:0'), tensor(0.9620, device='cuda:0'), tensor(0.9616, device='cuda:0'), tensor(0.9621, device='cuda:0'), tensor(0.9627, device='cuda:0'), tensor(0.9562, device='cuda:0')]

Output distance: [tensor(97.3600, device='cuda:0'), tensor(103.7061, device='cuda:0'), tensor(97.7574, device='cuda:0'), tensor(106.3153, device='cuda:0'), tensor(101.1285, device='cuda:0'), tensor(97.0504, device='cuda:0'), tensor(97.3651, device='cuda:0'), tensor(97.3654, device='cuda:0'), tensor(94.7778, device='cuda:0'), tensor(117.6759, device='cuda:0')]

Prediction loss: [tensor(366.5332, device='cuda:0'), tensor(371.6508, device='cuda:0'), tensor(374.3773, device='cuda:0'), tensor(386.5852, device='cuda:0'), tensor(380.6017, device='cuda:0'), tensor(374.9506, device='cuda:0'), tensor(368.9860, device='cuda:0'), tensor(372.2882, device='cuda:0'), tensor(369.5201, device='cuda:0'), tensor(373.0724, 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': 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')}, {'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(3509217., device='cuda:0'), tensor(3565946.7500, device='cuda:0'), tensor(3600088.5000, device='cuda:0'), tensor(3726706.7500, device='cuda:0'), tensor(3649717.7500, device='cuda:0'), tensor(3595061.2500, device='cuda:0'), tensor(3545322.5000, device='cuda:0'), tensor(3567215.7500, device='cuda:0'), tensor(3539835., device='cuda:0'), tensor(3599307.5000, device='cuda:0')]

Training loss: 3611092.0

Prediction time: [datetime.timedelta(microseconds=809568), datetime.timedelta(microseconds=847402), datetime.timedelta(microseconds=826495), datetime.timedelta(microseconds=850394), datetime.timedelta(microseconds=919102), datetime.timedelta(microseconds=843423), datetime.timedelta(microseconds=836462), datetime.timedelta(microseconds=831474), datetime.timedelta(microseconds=832470), datetime.timedelta(microseconds=931050)]

Phi time: [datetime.timedelta(seconds=1, microseconds=515860), datetime.timedelta(microseconds=986587), datetime.timedelta(microseconds=950194), datetime.timedelta(microseconds=974124), datetime.timedelta(microseconds=951839), datetime.timedelta(microseconds=945923), datetime.timedelta(microseconds=944863), datetime.timedelta(microseconds=960064), datetime.timedelta(microseconds=952219), datetime.timedelta(microseconds=950671)]

