Precision: [tensor(0.1667, device='cuda:0'), tensor(0.1705, device='cuda:0'), tensor(0.1631, device='cuda:0'), tensor(0.1443, device='cuda:0'), tensor(0.1756, device='cuda:0'), tensor(0.1900, device='cuda:0'), tensor(0.1436, device='cuda:0'), tensor(0.1412, device='cuda:0'), tensor(0.1500, device='cuda:0'), tensor(0.1489, device='cuda:0')]

Output distance: [tensor(21.0254, device='cuda:0'), tensor(21.0021, device='cuda:0'), tensor(21.0466, device='cuda:0'), tensor(21.1593, device='cuda:0'), tensor(20.9716, device='cuda:0'), tensor(20.8854, device='cuda:0'), tensor(21.1638, device='cuda:0'), tensor(21.1784, device='cuda:0'), tensor(21.1252, device='cuda:0'), tensor(21.1318, device='cuda:0')]

Prediction loss: [tensor(105.0106, device='cuda:0'), tensor(102.1663, device='cuda:0'), tensor(104.7731, device='cuda:0'), tensor(100.1259, device='cuda:0'), tensor(102.8968, device='cuda:0'), tensor(102.7625, device='cuda:0'), tensor(100.2554, device='cuda:0'), tensor(100.7881, device='cuda:0'), tensor(102.1904, device='cuda:0'), tensor(101.5794, device='cuda:0')]

Others: [{'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'num_positive': tensor(19848, 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(0.0001, 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=1, microseconds=729711), datetime.timedelta(seconds=1, microseconds=671954), datetime.timedelta(seconds=1, microseconds=692869), datetime.timedelta(seconds=1, microseconds=678925), datetime.timedelta(seconds=1, microseconds=675938), datetime.timedelta(seconds=1, microseconds=685897), datetime.timedelta(seconds=1, microseconds=699838), datetime.timedelta(seconds=1, microseconds=689881), datetime.timedelta(seconds=1, microseconds=685897), datetime.timedelta(seconds=1, microseconds=703822)]

Phi time: [datetime.timedelta(seconds=4, microseconds=423854), datetime.timedelta(seconds=4, microseconds=42988), datetime.timedelta(seconds=4, microseconds=57843), datetime.timedelta(seconds=4, microseconds=39802), datetime.timedelta(seconds=4, microseconds=99650), datetime.timedelta(seconds=4, microseconds=77112), datetime.timedelta(seconds=4, microseconds=70448), datetime.timedelta(seconds=4, microseconds=56182), datetime.timedelta(seconds=4, microseconds=55954), datetime.timedelta(seconds=4, microseconds=73984)]

