Precision: [tensor(0.4784, device='cuda:0'), tensor(0.4840, device='cuda:0'), tensor(0.4821, device='cuda:0'), tensor(0.4844, device='cuda:0'), tensor(0.4839, device='cuda:0'), tensor(0.4838, device='cuda:0'), tensor(0.4839, device='cuda:0'), tensor(0.4825, device='cuda:0'), tensor(0.4828, device='cuda:0'), tensor(0.4811, device='cuda:0')]

Output distance: [tensor(5.4355, device='cuda:0'), tensor(5.4019, device='cuda:0'), tensor(5.4135, device='cuda:0'), tensor(5.3998, device='cuda:0'), tensor(5.4030, device='cuda:0'), tensor(5.4035, device='cuda:0'), tensor(5.4025, device='cuda:0'), tensor(5.4114, device='cuda:0'), tensor(5.4093, device='cuda:0'), tensor(5.4198, device='cuda:0')]

Prediction loss: [tensor(16996886., device='cuda:0'), tensor(18971038., device='cuda:0'), tensor(16087924., device='cuda:0'), tensor(18423720., device='cuda:0'), tensor(17659010., device='cuda:0'), tensor(21436994., device='cuda:0'), tensor(19864236., device='cuda:0'), tensor(18380530., device='cuda:0'), tensor(17194324., device='cuda:0'), tensor(17366236., device='cuda:0')]

Others: [{'iter_num': 11, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(40797.4922, device='cuda:0'), tensor(40871.1055, device='cuda:0'), tensor(40677.2148, device='cuda:0'), tensor(40536.3008, device='cuda:0'), tensor(40527.2812, device='cuda:0'), tensor(41056.7227, device='cuda:0'), tensor(40585.2500, device='cuda:0'), tensor(40919.1133, device='cuda:0'), tensor(40746.3828, device='cuda:0'), tensor(40606.5742, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=62849), datetime.timedelta(seconds=1, microseconds=17110), datetime.timedelta(seconds=1, microseconds=11112), datetime.timedelta(seconds=1, microseconds=31825), datetime.timedelta(seconds=1, microseconds=4413), datetime.timedelta(seconds=1, microseconds=69395), datetime.timedelta(seconds=1, microseconds=3469), datetime.timedelta(seconds=1, microseconds=49315), datetime.timedelta(seconds=1, microseconds=2150), datetime.timedelta(microseconds=995358)]

Phi time: [datetime.timedelta(microseconds=181084), datetime.timedelta(microseconds=196307), datetime.timedelta(microseconds=189779), datetime.timedelta(microseconds=180488), datetime.timedelta(microseconds=181540), datetime.timedelta(microseconds=164534), datetime.timedelta(microseconds=193409), datetime.timedelta(microseconds=165459), datetime.timedelta(microseconds=186103), datetime.timedelta(microseconds=179416)]

