Precision: [tensor(0.6763, device='cuda:0'), tensor(0.6829, device='cuda:0'), tensor(0.6813, device='cuda:0'), tensor(0.6881, device='cuda:0'), tensor(0.6829, device='cuda:0'), tensor(0.6760, device='cuda:0'), tensor(0.6792, device='cuda:0'), tensor(0.6928, device='cuda:0'), tensor(0.6878, device='cuda:0'), tensor(0.6818, device='cuda:0')]

Output distance: [tensor(4.9535, device='cuda:0'), tensor(4.9404, device='cuda:0'), tensor(4.9436, device='cuda:0'), tensor(4.9299, device='cuda:0'), tensor(4.9404, device='cuda:0'), tensor(4.9541, device='cuda:0'), tensor(4.9478, device='cuda:0'), tensor(4.9205, device='cuda:0'), tensor(4.9304, device='cuda:0'), tensor(4.9425, device='cuda:0')]

Prediction loss: [tensor(19555190., device='cuda:0'), tensor(18185586., device='cuda:0'), tensor(17225444., device='cuda:0'), tensor(18927666., device='cuda:0'), tensor(19418764., device='cuda:0'), tensor(18735674., device='cuda:0'), tensor(18941660., device='cuda:0'), tensor(16296953., device='cuda:0'), tensor(17778696., device='cuda:0'), tensor(19447674., device='cuda:0')]

Others: [{'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, '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': 5, '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': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(40588.6133, device='cuda:0'), tensor(40884.5039, device='cuda:0'), tensor(40705.9883, device='cuda:0'), tensor(40721.5117, device='cuda:0'), tensor(40925.2969, device='cuda:0'), tensor(40716.8789, device='cuda:0'), tensor(40730.2930, device='cuda:0'), tensor(40647.4609, device='cuda:0'), tensor(40739.2109, device='cuda:0'), tensor(40789.1992, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(microseconds=972177), datetime.timedelta(microseconds=974561), datetime.timedelta(microseconds=973319), datetime.timedelta(microseconds=998105), datetime.timedelta(microseconds=974613), datetime.timedelta(microseconds=984589), datetime.timedelta(microseconds=972387), datetime.timedelta(microseconds=965872), datetime.timedelta(seconds=1, microseconds=83024), datetime.timedelta(microseconds=970206)]

Phi time: [datetime.timedelta(microseconds=210233), datetime.timedelta(microseconds=207829), datetime.timedelta(microseconds=204678), datetime.timedelta(microseconds=205857), datetime.timedelta(microseconds=223268), datetime.timedelta(microseconds=205621), datetime.timedelta(microseconds=209320), datetime.timedelta(microseconds=206691), datetime.timedelta(microseconds=216966), datetime.timedelta(microseconds=203834)]

