Precision: [tensor(0.5276, device='cuda:0'), tensor(0.5234, device='cuda:0'), tensor(0.5261, device='cuda:0'), tensor(0.5231, device='cuda:0'), tensor(0.5240, device='cuda:0'), tensor(0.5240, device='cuda:0'), tensor(0.5221, device='cuda:0'), tensor(0.5218, device='cuda:0'), tensor(0.5233, device='cuda:0'), tensor(0.5236, device='cuda:0')]

Output distance: [tensor(5.1405, device='cuda:0'), tensor(5.1657, device='cuda:0'), tensor(5.1494, device='cuda:0'), tensor(5.1678, device='cuda:0'), tensor(5.1620, device='cuda:0'), tensor(5.1620, device='cuda:0'), tensor(5.1735, device='cuda:0'), tensor(5.1751, device='cuda:0'), tensor(5.1662, device='cuda:0'), tensor(5.1646, device='cuda:0')]

Prediction loss: [tensor(19158056., device='cuda:0'), tensor(19487390., device='cuda:0'), tensor(18583352., device='cuda:0'), tensor(17371942., device='cuda:0'), tensor(19038842., device='cuda:0'), tensor(20809670., device='cuda:0'), tensor(16767067., device='cuda:0'), tensor(18493418., device='cuda:0'), tensor(18143564., device='cuda:0'), tensor(17333216., device='cuda:0')]

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

Compressed training loss: [tensor(40728.1328, device='cuda:0'), tensor(40957.9258, device='cuda:0'), tensor(40923.8789, device='cuda:0'), tensor(40717.2617, device='cuda:0'), tensor(40765.0508, device='cuda:0'), tensor(41003.2422, device='cuda:0'), tensor(40817.8516, device='cuda:0'), tensor(40821.0664, device='cuda:0'), tensor(41127.9414, device='cuda:0'), tensor(40593.5195, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=14383), datetime.timedelta(seconds=1, microseconds=10290), datetime.timedelta(seconds=1, microseconds=29667), datetime.timedelta(seconds=1, microseconds=33493), datetime.timedelta(seconds=1, microseconds=61282), datetime.timedelta(seconds=1, microseconds=50068), datetime.timedelta(seconds=1, microseconds=43600), datetime.timedelta(seconds=1, microseconds=37516), datetime.timedelta(seconds=1, microseconds=22860), datetime.timedelta(seconds=1, microseconds=18894)]

Phi time: [datetime.timedelta(microseconds=193254), datetime.timedelta(microseconds=194937), datetime.timedelta(microseconds=185890), datetime.timedelta(microseconds=182128), datetime.timedelta(microseconds=204271), datetime.timedelta(microseconds=200743), datetime.timedelta(microseconds=195413), datetime.timedelta(microseconds=199978), datetime.timedelta(microseconds=203896), datetime.timedelta(microseconds=196704)]

