Precision: [tensor(0.5325, device='cuda:0'), tensor(0.5293, device='cuda:0'), tensor(0.5285, device='cuda:0'), tensor(0.5329, device='cuda:0'), tensor(0.5343, device='cuda:0'), tensor(0.5305, device='cuda:0'), tensor(0.5285, device='cuda:0'), tensor(0.5273, device='cuda:0'), tensor(0.5288, device='cuda:0'), tensor(0.5308, device='cuda:0')]

Output distance: [tensor(5.1111, device='cuda:0'), tensor(5.1305, device='cuda:0'), tensor(5.1352, device='cuda:0'), tensor(5.1090, device='cuda:0'), tensor(5.1000, device='cuda:0'), tensor(5.1231, device='cuda:0'), tensor(5.1352, device='cuda:0'), tensor(5.1420, device='cuda:0'), tensor(5.1331, device='cuda:0'), tensor(5.1210, device='cuda:0')]

Prediction loss: [tensor(21064610., device='cuda:0'), tensor(18476650., device='cuda:0'), tensor(17679910., device='cuda:0'), tensor(18659690., device='cuda:0'), tensor(18842698., device='cuda:0'), tensor(18161780., device='cuda:0'), tensor(18562346., device='cuda:0'), tensor(18222376., device='cuda:0'), tensor(19604128., device='cuda:0'), tensor(18365858., device='cuda:0')]

Others: [{'iter_num': 30, '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': 30, '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': 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': 30, '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': 30, '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(40746.9414, device='cuda:0'), tensor(40903.0078, device='cuda:0'), tensor(40871.3438, device='cuda:0'), tensor(40794.9023, device='cuda:0'), tensor(41120.4766, device='cuda:0'), tensor(40795.4062, device='cuda:0'), tensor(40723.0625, device='cuda:0'), tensor(40992.4141, device='cuda:0'), tensor(40823.5391, device='cuda:0'), tensor(40861.7578, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=173591), datetime.timedelta(seconds=1, microseconds=177237), datetime.timedelta(seconds=1, microseconds=181130), datetime.timedelta(seconds=1, microseconds=162565), datetime.timedelta(seconds=1, microseconds=50218), datetime.timedelta(seconds=1, microseconds=51822), datetime.timedelta(seconds=1, microseconds=151423), datetime.timedelta(seconds=1, microseconds=164581), datetime.timedelta(seconds=1, microseconds=171002), datetime.timedelta(seconds=1, microseconds=31233)]

Phi time: [datetime.timedelta(microseconds=191499), datetime.timedelta(microseconds=196240), datetime.timedelta(microseconds=221666), datetime.timedelta(microseconds=209178), datetime.timedelta(microseconds=181108), datetime.timedelta(microseconds=178583), datetime.timedelta(microseconds=211106), datetime.timedelta(microseconds=192046), datetime.timedelta(microseconds=199910), datetime.timedelta(microseconds=190169)]

