Precision: [tensor(0.5641, device='cuda:0'), tensor(0.5664, device='cuda:0'), tensor(0.5651, device='cuda:0'), tensor(0.5642, device='cuda:0'), tensor(0.5621, device='cuda:0'), tensor(0.5645, device='cuda:0'), tensor(0.5636, device='cuda:0'), tensor(0.5652, device='cuda:0'), tensor(0.5660, device='cuda:0'), tensor(0.5631, device='cuda:0')]

Output distance: [tensor(4.9215, device='cuda:0'), tensor(4.9078, device='cuda:0'), tensor(4.9157, device='cuda:0'), tensor(4.9210, device='cuda:0'), tensor(4.9336, device='cuda:0'), tensor(4.9189, device='cuda:0'), tensor(4.9247, device='cuda:0'), tensor(4.9147, device='cuda:0'), tensor(4.9099, device='cuda:0'), tensor(4.9278, device='cuda:0')]

Prediction loss: [tensor(18920850., device='cuda:0'), tensor(18485434., device='cuda:0'), tensor(18075828., device='cuda:0'), tensor(19210716., device='cuda:0'), tensor(20169962., device='cuda:0'), tensor(18394906., device='cuda:0'), tensor(17976646., device='cuda:0'), tensor(17741836., device='cuda:0'), tensor(18409936., device='cuda:0'), tensor(19006808., device='cuda:0')]

Others: [{'iter_num': 7, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(40810.2617, device='cuda:0'), tensor(40749.6641, device='cuda:0'), tensor(40794.4102, device='cuda:0'), tensor(40755.5195, device='cuda:0'), tensor(40799.3633, device='cuda:0'), tensor(40910.4062, device='cuda:0'), tensor(40813.3828, device='cuda:0'), tensor(40806.8359, device='cuda:0'), tensor(40851.1836, device='cuda:0'), tensor(40823.5977, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=63275), datetime.timedelta(seconds=1, microseconds=23249), datetime.timedelta(seconds=1, microseconds=55994), datetime.timedelta(seconds=1, microseconds=270325), datetime.timedelta(seconds=1, microseconds=32599), datetime.timedelta(seconds=1, microseconds=301590), datetime.timedelta(seconds=1, microseconds=54053), datetime.timedelta(seconds=1, microseconds=60432), datetime.timedelta(seconds=1, microseconds=48025), datetime.timedelta(seconds=1, microseconds=288578)]

Phi time: [datetime.timedelta(microseconds=293220), datetime.timedelta(microseconds=297897), datetime.timedelta(microseconds=293388), datetime.timedelta(microseconds=296464), datetime.timedelta(microseconds=292163), datetime.timedelta(microseconds=298883), datetime.timedelta(microseconds=307778), datetime.timedelta(microseconds=290682), datetime.timedelta(microseconds=290836), datetime.timedelta(microseconds=299992)]

