Precision: [tensor(0.9995, device='cuda:0'), tensor(0.9998, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9998, device='cuda:0'), tensor(0.9998, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9997, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9997, device='cuda:0')]

Output distance: [tensor(23610.2402, device='cuda:0'), tensor(24136.6992, device='cuda:0'), tensor(23459.2949, device='cuda:0'), tensor(23615.7031, device='cuda:0'), tensor(23830.0508, device='cuda:0'), tensor(23622.9043, device='cuda:0'), tensor(23669.5254, device='cuda:0'), tensor(23512.3887, device='cuda:0'), tensor(24307.4180, device='cuda:0'), tensor(23621.5977, device='cuda:0')]

Prediction loss: [tensor(24574.3066, device='cuda:0'), tensor(21632.1992, device='cuda:0'), tensor(24315.7363, device='cuda:0'), tensor(23588.7324, device='cuda:0'), tensor(22058.1777, device='cuda:0'), tensor(21897.4082, device='cuda:0'), tensor(25409.5859, device='cuda:0'), tensor(22496.1172, device='cuda:0'), tensor(24388.2402, device='cuda:0'), tensor(25505.3652, device='cuda:0')]

Others: [{'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, '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': 11, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, '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')}]

Compressed training loss: [tensor(9039060., device='cuda:0'), tensor(8457340., device='cuda:0'), tensor(9088435., device='cuda:0'), tensor(8883481., device='cuda:0'), tensor(8689419., device='cuda:0'), tensor(8554947., device='cuda:0'), tensor(9189342., device='cuda:0'), tensor(8749412., device='cuda:0'), tensor(8954871., device='cuda:0'), tensor(9531339., device='cuda:0')]

Training loss: 8846330.0

Prediction time: [datetime.timedelta(microseconds=549691), datetime.timedelta(microseconds=753833), datetime.timedelta(microseconds=638318), datetime.timedelta(microseconds=644296), datetime.timedelta(microseconds=654251), datetime.timedelta(microseconds=689102), datetime.timedelta(microseconds=581557), datetime.timedelta(microseconds=639314), datetime.timedelta(microseconds=640308), datetime.timedelta(microseconds=579564)]

Phi time: [datetime.timedelta(seconds=1, microseconds=328112), datetime.timedelta(microseconds=793720), datetime.timedelta(microseconds=734482), datetime.timedelta(microseconds=725875), datetime.timedelta(microseconds=739891), datetime.timedelta(microseconds=738896), datetime.timedelta(microseconds=733810), datetime.timedelta(microseconds=729915), datetime.timedelta(microseconds=728762), datetime.timedelta(microseconds=726686)]

