Precision: [tensor(0.0963, device='cuda:0'), tensor(0.0733, device='cuda:0'), tensor(0.0892, device='cuda:0'), tensor(0.0913, device='cuda:0'), tensor(0.1002, device='cuda:0'), tensor(0.0580, device='cuda:0'), tensor(0.0942, device='cuda:0'), tensor(0.0904, device='cuda:0'), tensor(0.0887, device='cuda:0'), tensor(0.0804, device='cuda:0')]

Output distance: [tensor(19.8328, device='cuda:0'), tensor(19.8788, device='cuda:0'), tensor(19.8470, device='cuda:0'), tensor(19.8428, device='cuda:0'), tensor(19.8250, device='cuda:0'), tensor(19.9093, device='cuda:0'), tensor(19.8371, device='cuda:0'), tensor(19.8446, device='cuda:0'), tensor(19.8479, device='cuda:0'), tensor(19.8646, device='cuda:0')]

Prediction loss: [tensor(109.1486, device='cuda:0'), tensor(107.1602, device='cuda:0'), tensor(106.3228, device='cuda:0'), tensor(108.0219, device='cuda:0'), tensor(105.9580, device='cuda:0'), tensor(103.2524, device='cuda:0'), tensor(104.7894, device='cuda:0'), tensor(106.9769, device='cuda:0'), tensor(107.8077, device='cuda:0'), tensor(105.9919, device='cuda:0')]

Others: [{'iter_num': 15, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 17, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}]

Compressed training loss: [tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(9.9330e-05, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=2, microseconds=554329), datetime.timedelta(seconds=2, microseconds=550019), datetime.timedelta(seconds=2, microseconds=576359), datetime.timedelta(seconds=2, microseconds=614078), datetime.timedelta(seconds=2, microseconds=495713), datetime.timedelta(seconds=2, microseconds=557193), datetime.timedelta(seconds=2, microseconds=567662), datetime.timedelta(seconds=2, microseconds=559275), datetime.timedelta(seconds=2, microseconds=513215), datetime.timedelta(seconds=2, microseconds=567427)]

Phi time: [datetime.timedelta(seconds=174, microseconds=493710), datetime.timedelta(seconds=174, microseconds=899040), datetime.timedelta(seconds=174, microseconds=499042), datetime.timedelta(seconds=173, microseconds=641271), datetime.timedelta(seconds=173, microseconds=623915), datetime.timedelta(seconds=173, microseconds=756401), datetime.timedelta(seconds=174, microseconds=849073), datetime.timedelta(seconds=174, microseconds=668650), datetime.timedelta(seconds=174, microseconds=528915), datetime.timedelta(seconds=174, microseconds=686808)]

