Precision: [tensor(0.6760, device='cuda:0'), tensor(0.6789, device='cuda:0'), tensor(0.6831, device='cuda:0'), tensor(0.6758, device='cuda:0'), tensor(0.6871, device='cuda:0'), tensor(0.6831, device='cuda:0'), tensor(0.6813, device='cuda:0'), tensor(0.6810, device='cuda:0'), tensor(0.6771, device='cuda:0'), tensor(0.6802, device='cuda:0')]

Output distance: [tensor(4.9541, device='cuda:0'), tensor(4.9483, device='cuda:0'), tensor(4.9399, device='cuda:0'), tensor(4.9546, device='cuda:0'), tensor(4.9320, device='cuda:0'), tensor(4.9399, device='cuda:0'), tensor(4.9436, device='cuda:0'), tensor(4.9441, device='cuda:0'), tensor(4.9520, device='cuda:0'), tensor(4.9457, device='cuda:0')]

Prediction loss: [tensor(19497858., device='cuda:0'), tensor(18276644., device='cuda:0'), tensor(18464676., device='cuda:0'), tensor(17543508., device='cuda:0'), tensor(18081352., device='cuda:0'), tensor(19060808., device='cuda:0'), tensor(19769718., device='cuda:0'), tensor(17969962., device='cuda:0'), tensor(16692923., device='cuda:0'), tensor(16591196., device='cuda:0')]

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

Compressed training loss: [tensor(40788.6953, device='cuda:0'), tensor(40764.8516, device='cuda:0'), tensor(40732.0039, device='cuda:0'), tensor(41069.1367, device='cuda:0'), tensor(40865.7070, device='cuda:0'), tensor(40915.7461, device='cuda:0'), tensor(40753.0547, device='cuda:0'), tensor(40860.4609, device='cuda:0'), tensor(40868.8320, device='cuda:0'), tensor(40858.1914, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(microseconds=993005), datetime.timedelta(microseconds=999378), datetime.timedelta(microseconds=983645), datetime.timedelta(microseconds=954596), datetime.timedelta(microseconds=971857), datetime.timedelta(microseconds=975847), datetime.timedelta(microseconds=980379), datetime.timedelta(microseconds=966669), datetime.timedelta(microseconds=993616), datetime.timedelta(microseconds=980524)]

Phi time: [datetime.timedelta(microseconds=205993), datetime.timedelta(microseconds=219763), datetime.timedelta(microseconds=221469), datetime.timedelta(microseconds=205692), datetime.timedelta(microseconds=214103), datetime.timedelta(microseconds=225938), datetime.timedelta(microseconds=216543), datetime.timedelta(microseconds=221944), datetime.timedelta(microseconds=198856), datetime.timedelta(microseconds=199907)]

