Precision: [tensor(0.9586, device='cuda:0'), tensor(0.9593, device='cuda:0'), tensor(0.9581, device='cuda:0'), tensor(0.9556, device='cuda:0'), tensor(0.9559, device='cuda:0'), tensor(0.9554, device='cuda:0'), tensor(0.9596, device='cuda:0'), tensor(0.9592, device='cuda:0'), tensor(0.9557, device='cuda:0'), tensor(0.9579, device='cuda:0')]

Output distance: [tensor(108.4872, device='cuda:0'), tensor(102.4202, device='cuda:0'), tensor(107.0340, device='cuda:0'), tensor(116.5037, device='cuda:0'), tensor(112.4979, device='cuda:0'), tensor(115.2430, device='cuda:0'), tensor(101.1151, device='cuda:0'), tensor(103.6812, device='cuda:0'), tensor(113.4029, device='cuda:0'), tensor(107.6855, device='cuda:0')]

Prediction loss: [tensor(383.0990, device='cuda:0'), tensor(396.6740, device='cuda:0'), tensor(385.2713, device='cuda:0'), tensor(381.3419, device='cuda:0'), tensor(370.7251, device='cuda:0'), tensor(389.7025, device='cuda:0'), tensor(384.3347, device='cuda:0'), tensor(379.5942, device='cuda:0'), tensor(376.0335, device='cuda:0'), tensor(358.3241, device='cuda:0')]

Others: [{'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(3703414., device='cuda:0'), tensor(3821766.2500, device='cuda:0'), tensor(3722543., device='cuda:0'), tensor(3708390., device='cuda:0'), tensor(3597866.7500, device='cuda:0'), tensor(3788784.2500, device='cuda:0'), tensor(3709023.7500, device='cuda:0'), tensor(3666576., device='cuda:0'), tensor(3636740.7500, device='cuda:0'), tensor(3465985.2500, device='cuda:0')]

Training loss: 3615430.25

Prediction time: [datetime.timedelta(microseconds=859356), datetime.timedelta(microseconds=837449), datetime.timedelta(microseconds=875246), datetime.timedelta(microseconds=800603), datetime.timedelta(microseconds=906156), datetime.timedelta(microseconds=800605), datetime.timedelta(microseconds=786664), datetime.timedelta(microseconds=872300), datetime.timedelta(microseconds=804589), datetime.timedelta(microseconds=866325)]

Phi time: [datetime.timedelta(seconds=1, microseconds=453367), datetime.timedelta(microseconds=926149), datetime.timedelta(microseconds=864610), datetime.timedelta(microseconds=863648), datetime.timedelta(microseconds=898538), datetime.timedelta(microseconds=870101), datetime.timedelta(microseconds=865708), datetime.timedelta(microseconds=869175), datetime.timedelta(microseconds=868182), datetime.timedelta(microseconds=862721)]

