Precision: [tensor(0.5391, device='cuda:0'), tensor(0.5371, device='cuda:0'), tensor(0.5335, device='cuda:0'), tensor(0.5349, device='cuda:0'), tensor(0.5379, device='cuda:0'), tensor(0.5386, device='cuda:0'), tensor(0.5364, device='cuda:0'), tensor(0.5369, device='cuda:0'), tensor(0.5353, device='cuda:0'), tensor(0.5412, device='cuda:0')]

Output distance: [tensor(5.0717, device='cuda:0'), tensor(5.0837, device='cuda:0'), tensor(5.1053, device='cuda:0'), tensor(5.0969, device='cuda:0'), tensor(5.0785, device='cuda:0'), tensor(5.0743, device='cuda:0'), tensor(5.0874, device='cuda:0'), tensor(5.0848, device='cuda:0'), tensor(5.0943, device='cuda:0'), tensor(5.0591, device='cuda:0')]

Prediction loss: [tensor(19102260., device='cuda:0'), tensor(18808268., device='cuda:0'), tensor(18914138., device='cuda:0'), tensor(19730200., device='cuda:0'), tensor(19638580., device='cuda:0'), tensor(18530618., device='cuda:0'), tensor(18786632., device='cuda:0'), tensor(18469532., device='cuda:0'), tensor(19607446., device='cuda:0'), tensor(18821848., device='cuda:0')]

Others: [{'iter_num': 11, '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': 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': 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')}]

Compressed training loss: [tensor(40814.5156, device='cuda:0'), tensor(40831.7461, device='cuda:0'), tensor(40819.4570, device='cuda:0'), tensor(40744.3750, device='cuda:0'), tensor(40956.0039, device='cuda:0'), tensor(40876.3164, device='cuda:0'), tensor(40811.1680, device='cuda:0'), tensor(40918.5664, device='cuda:0'), tensor(40978.4688, device='cuda:0'), tensor(40738.8125, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=40181), datetime.timedelta(microseconds=999889), datetime.timedelta(seconds=1, microseconds=22383), datetime.timedelta(seconds=1, microseconds=44483), datetime.timedelta(seconds=1, microseconds=18610), datetime.timedelta(seconds=1, microseconds=42407), datetime.timedelta(seconds=1, microseconds=27718), datetime.timedelta(seconds=1, microseconds=25334), datetime.timedelta(microseconds=996194), datetime.timedelta(seconds=1, microseconds=26084)]

Phi time: [datetime.timedelta(microseconds=218939), datetime.timedelta(microseconds=202636), datetime.timedelta(microseconds=202694), datetime.timedelta(microseconds=199858), datetime.timedelta(microseconds=227689), datetime.timedelta(microseconds=199975), datetime.timedelta(microseconds=222268), datetime.timedelta(microseconds=201027), datetime.timedelta(microseconds=203783), datetime.timedelta(microseconds=223927)]

